Estimating Treatment Effect of Adjuvant Chemotherapy in Elderly Patients With Stage III Colon Cancer Using Bayesian Networks.


Journal

JCO clinical cancer informatics
ISSN: 2473-4276
Titre abrégé: JCO Clin Cancer Inform
Pays: United States
ID NLM: 101708809

Informations de publication

Date de publication:
09 2023
Historique:
pmc-release: 25 09 2024
medline: 27 9 2023
pubmed: 25 9 2023
entrez: 25 9 2023
Statut: ppublish

Résumé

While adjuvant therapy with capecitabine and oxaliplatin (CAPOX) has been proven to be effective in stage III colon cancer, capecitabine monotherapy (CapMono) might be equally effective in elderly patients. Unfortunately, the elderly are under-represented in clinical trials and patients included may not be representative of the routine care population. Observational data might alleviate this problem but is sensitive to biases such as confounding by indication. Here, we build causal models using Bayesian Networks (BNs), identify confounders, and estimate the effect of adjuvant chemotherapy using survival analyses. Patients 70 years and older were selected from the Netherlands Cancer Registry (N = 982). We developed several BNs using constraint-based, score-based, and hybrid algorithms while precluding noncausal relations. In addition, we created models using a limited set of recurrence and survival nodes. Potential confounders were identified through the resulting graphs. Several Cox models were fitted correcting for confounders and for propensity scores. When comparing adjuvant treatment with surgery only, pathological lymph node classification, physical status, and age were identified as potential confounders. Adjuvant treatment was significantly associated with survival in all Cox models, with hazard ratios between 0.39 and 0.45; CIs overlapped. BNs investigating CAPOX versus CapMono did not find any association between the treatment choice and survival and thus no confounders. Analyses using Cox models did not identify significant association either. We were able to successfully leverage BN structure learning algorithms in conjunction with clinical knowledge to create causal models. While confounders differed depending on the algorithm and included nodes, results were not contradictory. We found a strong effect of adjuvant therapy on survival in our cohort. Additional oxaliplatin did not have a marked effect and should be avoided in elderly patients.

Identifiants

pubmed: 37748112
doi: 10.1200/CCI.23.00080
pmc: PMC10569780
doi:

Substances chimiques

Capecitabine 6804DJ8Z9U
Oxaliplatin 04ZR38536J

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e2300080

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Auteurs

Melle Sieswerda (M)

Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands.
Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands.

Ruby van Rossum (R)

Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands.

Inigo Bermejo (I)

Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands.

Gijs Geleijnse (G)

Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands.

Katja Aben (K)

Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands.
Department for Health Evidence, Radboud University Medical Centre, Nijmegen, the Netherlands.

Felice van Erning (F)

Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands.
Department of Surgery, Catharina Hospital, Eindhoven, the Netherlands.

Ignace de Hingh (I)

Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands.
Department of Surgery, Catharina Hospital, Eindhoven, the Netherlands.

Valery Lemmens (V)

Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands.

André Dekker (A)

Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands.

Xander Verbeek (X)

Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands.

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