1
|
Mahon P, Hall G, Dekker A, Vehreschild J, Tonon G. Harnessing oncology real-world data with AI. NATURE CANCER 2023; 4:1627-1629. [PMID: 38102358 DOI: 10.1038/s43018-023-00689-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Affiliation(s)
- Piers Mahon
- Digital Institute for Cancer Outcomes Research E.E.I.G, Brussels, Belgium
- IQVIA Cancer Research BV, Brussels, Belgium
| | - Geoff Hall
- Leeds Teaching Hospital NHS Trust, Leeds, UK
- Health Data Research, London, UK
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Janne Vehreschild
- University Hospital of Frankfurt, Center for Internal Medicine, Department II - Hematology/Oncology, Frankfurt, Germany
| | - Giovanni Tonon
- Center for Omics Sciences, IRCCS Ospedale San Raffaele, Milan, Italy.
- Università Vita-Salute San Raffaele, Milan, Italy.
| |
Collapse
|
2
|
Sieswerda M, van Rossum R, Bermejo I, Geleijnse G, Aben K, van Erning F, de Hingh I, Lemmens V, Dekker A, Verbeek X. Estimating Treatment Effect of Adjuvant Chemotherapy in Elderly Patients With Stage III Colon Cancer Using Bayesian Networks. JCO Clin Cancer Inform 2023; 7:e2300080. [PMID: 37748112 PMCID: PMC10569780 DOI: 10.1200/cci.23.00080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 07/13/2023] [Accepted: 07/31/2023] [Indexed: 09/27/2023] Open
Abstract
PURPOSE 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. METHODS 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. RESULTS 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. CONCLUSION 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.
Collapse
Affiliation(s)
- Melle Sieswerda
- 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
- Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands
| | - Inigo Bermejo
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Gijs Geleijnse
- Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands
| | - Katja Aben
- 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
- Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands
- Department of Surgery, Catharina Hospital, Eindhoven, the Netherlands
| | - Ignace de Hingh
- Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands
- Department of Surgery, Catharina Hospital, Eindhoven, the Netherlands
| | - Valery Lemmens
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - André Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Xander Verbeek
- Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands
| |
Collapse
|