Secondary Cancers Among Patients with Multiple Myeloma: A 15-Year Analysis of a Population-Based Cohort in Ontario, Canada
Notice bibliographique
Résumé
Introduction: With improving overall survival among MM patients, there is an increasing need to both understand the absolute risk and identify risk factors for developing a second primary malignancy (SPM). Understanding these risk and potential modulation factors may help guide decisions regarding treatment options, monitoring surveillance strategies, and patient counselling. Furthermore, understanding this baseline SPM risk will help contextualize the future SPM risk associated with newer therapeutics, including chimeric antigen receptor therapies. Thus, we conducted a population-based study with following objectives: 1) to evaluate the rate and distribution of SPMs, and 2) to identify risk factors including a history of prior malignancy associated with an increased risk of SPMs in patients with MM. Methods: We conducted a retrospective population-based study using data from Institute for Clinical Evaluative Sciences (ICES), an administrative database that captures all health records in the publicly funded health care system in Ontario, Canada. Adult patients treated for newly diagnosed MM between 2007-2022 were identified using ICD-O-3 code 9732/3 (MM). The list of secondary cancers (excluding non-melanoma skin cancers) were identified from pathology codes maintained in the Cancer Care Ontario Registry. Additional risk factors were also collected including a history and type of any pre-existing cancer (at least 1 year prior to MM diagnosis), treatment details including history of autologous stem cell transplantation (ASCT) and lenalidomide usage. The Kaplan-Meier method was used to estimate the median overall survival (OS). We estimated the cumulative incidence of secondary cancer accounting for death as a competing risk. To identify the association of prognostic factors on the risk of developing a SPM, we performed competing risks regression and estimated the subdistribution hazard ratios (sHR) accounting for death as a competing risk. Results: A total of 12123 patients . The median age at diagnosis was 70 (IQR 61-77) years, 6860 (57%) of our cohort had a male sex. In our 4511 (37%) received at least one ASCT, 8062 (67%) were lenalidomide exposed and 6917 (57%) were cyclophosphamide exposed at any time post MM diagnosis, and 3017 (16%) received oral melphalan based induction regimens. The median follow up for cohort was 4.53 (95% CI 4.4-4.7) years. A total of 2079 patients (17%) had a prior cancer diagnosis at least 1 year prior to their MM diagnosis. Of these patients, 665 (32%) had received chemotherapy and 553 (27%) had received radiation to treat their prior malignancy. The three most common prior cancers were: prostate cancer (25%), breast cancer (13%) and heme malignancies (4%).The median time from diagnosis of a prior malignancy to MM diagnosis was 6.4 (IQR 2.04-13.26) years. A total of 1070 (8.8%) of the cohort developed a SPM. The cumulative incidence of SPM post MM diagnosis, accounting for death as a competing risk, was 3.7% at 2 years, 7.6% at 5 years, and 11.5% at 10 years. The three most common SPMs were hematologic (21%), prostate (7%), and lung (7%) cancer. The three most common hematological SPMs were acute myeloid leukemia (37%), B-acute lymphoblastic leukemia (16%), and myelodysplastic syndrome (19%). The median OS following after any SPM diagnosis was 1.64 (95% CI 1.36-1.92) years, was 1.85 (95% CI 1.54-2.28) years for patients that developed a non-hematologic SPM, and was 1.05 (95% CI 0.88-1.40) years for patients that developed a hematologic SPM. Conclusion: This study represents one of the largest real-world cohort studies examining the rates, risk factor and outcomes of MM patients developing secondary cancers. We show that a prior malignancy diagnosis increased the risk of SPM after adjusting for ASCT and lenalidomide exposure, sex, and age at MM diagnosis. These data are important for patients and physicians to understand the risks associated with treatment of MM.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,006 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».