Prediction of breast cancer risk for adolescents and young adults with Hodgkin lymphoma.


Journal

Journal of the National Cancer Institute
ISSN: 1460-2105
Titre abrégé: J Natl Cancer Inst
Pays: United States
ID NLM: 7503089

Informations de publication

Date de publication:
01 Nov 2024
Historique:
received: 18 06 2024
revised: 18 10 2024
accepted: 28 10 2024
medline: 1 11 2024
pubmed: 1 11 2024
entrez: 1 11 2024
Statut: aheadofprint

Résumé

While female survivors of Hodgkin lymphoma (HL) have an increased risk of breast cancer (BC), no BC risk prediction model is available. We developed such models incorporating mean radiation dose to the breast or breast quadrant-specific radiation doses. Relative risks and age-specific incidence for BC and competing events (mortality or other subsequent cancer) were estimated from 1194 Dutch five-year HL survivors, treated at ages 11-40 during 1965-2000. Predictors were doses to ten breast segments or mean breast radiation dose, BC family history, year of and age at HL diagnosis, ages at menopause and first live birth. Models were independently validated using U.S. Childhood Cancer Survivor Study cohort participants. Predicted absolute BC risks 25 years after HL diagnosis ranged from 1.0% for survivors diagnosed at ages 20-24, with <10 Gy mean breast radiation dose and menopausal 5 years after HL diagnosis, to 22.0% for survivors 25-29 years at diagnosis, ≥25 Gy mean breast dose, and no menopause within 5 years. In external validation, the observed/expected BC case ratio was 1.19 (95% confidence interval 0.97 to 1.47) for the breast segment-specific doses model, and 1.29 (1.05 to 1.60) for the mean breast dose model. The areas under the receiver operating characteristic curve were 0.68 (0.63 to 0.74) and 0.68 (0.62 to 0.73), respectively. Breast segment-specific or mean breast radiation dose with personal and clinical characteristics predicted absolute BC risk in HL survivors with moderate discrimination but good calibration, rendering the models useful for clinical decision-making.

Sections du résumé

BACKGROUND BACKGROUND
While female survivors of Hodgkin lymphoma (HL) have an increased risk of breast cancer (BC), no BC risk prediction model is available. We developed such models incorporating mean radiation dose to the breast or breast quadrant-specific radiation doses.
METHODS METHODS
Relative risks and age-specific incidence for BC and competing events (mortality or other subsequent cancer) were estimated from 1194 Dutch five-year HL survivors, treated at ages 11-40 during 1965-2000. Predictors were doses to ten breast segments or mean breast radiation dose, BC family history, year of and age at HL diagnosis, ages at menopause and first live birth. Models were independently validated using U.S. Childhood Cancer Survivor Study cohort participants.
RESULTS RESULTS
Predicted absolute BC risks 25 years after HL diagnosis ranged from 1.0% for survivors diagnosed at ages 20-24, with <10 Gy mean breast radiation dose and menopausal 5 years after HL diagnosis, to 22.0% for survivors 25-29 years at diagnosis, ≥25 Gy mean breast dose, and no menopause within 5 years. In external validation, the observed/expected BC case ratio was 1.19 (95% confidence interval 0.97 to 1.47) for the breast segment-specific doses model, and 1.29 (1.05 to 1.60) for the mean breast dose model. The areas under the receiver operating characteristic curve were 0.68 (0.63 to 0.74) and 0.68 (0.62 to 0.73), respectively.
CONCLUSION CONCLUSIONS
Breast segment-specific or mean breast radiation dose with personal and clinical characteristics predicted absolute BC risk in HL survivors with moderate discrimination but good calibration, rendering the models useful for clinical decision-making.

Identifiants

pubmed: 39485483
pii: 7863296
doi: 10.1093/jnci/djae274
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Auteurs

Sander Roberti (S)

Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.

Flora E van Leeuwen (FE)

Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.

Ibrahima Diallo (I)

University Paris Saclay, Villejuif, France.
Molecular Radiation Therapy and Therapeutic Innovation, INSERM U1030, Villejuif, France.
Radiation Oncology Department, Gustave Roussy, Villejuif, France.

Florent de Vathaire (F)

Radiation Oncology Department, Gustave Roussy, Villejuif, France.
Radiation Epidemiology Team, Center for Research in Epidemiology and Population Health, INSERM U1018, Villejuif, France.
Research Department, Gustave Roussy, Villejuif, France.

Michael Schaapveld (M)

Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.

Wendy M Leisenring (WM)

Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.

Rebecca M Howell (RM)

Department of Radiation Physics, University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America.

Gregory T Armstrong (GT)

Department of Epidemiology and Cancer Control, St Jude Children's Research Hospital, Memphis, Tennessee, United States of America.

Chaya S Moskowitz (CS)

Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.

Susan A Smith (SA)

Department of Radiation Physics, University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America.

Berthe M P Aleman (BMP)

Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.

Inge M Krul (IM)

Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.

Nicola S Russell (NS)

Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.

Ruth M Pfeiffer (RM)

National Cancer Institute, Division of Cancer Epidemiology and Genetics, Bethesda, Maryland, United States of America.

Michael Hauptmann (M)

Institute of Biostatistics and Registry Research, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany.

Classifications MeSH