Improving Adjuvant Liver-Directed Treatment Recommendations for Unresectable Hepatocellular Carcinoma: An Artificial Intelligence-Based Decision-Making Tool.

Autor:	Mo A; Department of Radiation Oncology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY., Velten C; Department of Radiation Oncology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY.; Institute for Onco-Physics, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY., Jiang JM; Department of Radiation Oncology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY., Tang J; Department of Radiation Oncology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY., Ohri N; Department of Radiation Oncology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY., Kalnicki S; Department of Radiation Oncology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY., Mirhaji P; Department of Systems & Computational Biology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY.; Center for Health Data Innovation, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY., Nemoto K; Center for Health Data Innovation, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY., Aasman B; Center for Health Data Innovation, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY., Garg M; Department of Radiation Oncology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY., Guha C; Department of Radiation Oncology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY.; Institute for Onco-Physics, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY., Brodin NP; Department of Radiation Oncology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY.; Institute for Onco-Physics, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY., Kabarriti R; Department of Radiation Oncology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY.
Jazyk:	angličtina
Zdroj:	JCO clinical cancer informatics [JCO Clin Cancer Inform] 2022 Jun; Vol. 6, pp. e2200024.
DOI:	10.1200/CCI.22.00024
Abstrakt:	Purpose: Liver-directed therapy after transarterial chemoembolization (TACE) can lead to improvement in survival for selected patients with unresectable hepatocellular carcinoma (HCC). However, there is uncertainty in the appropriate application and modality of therapy in current clinical practice guidelines. The aim of this study was to develop a proof-of-concept, machine learning (ML) model for treatment recommendation in patients previously treated with TACE and select patients who might benefit from additional treatment with combination stereotactic body radiotherapy (SBRT) or radiofrequency ablation (RFA). Methods: This retrospective observational study was based on data from an urban, academic hospital system selecting for patients diagnosed with stage I-III HCC from January 1, 2008, to December 31, 2018, treated with TACE, followed by adjuvant RFA, SBRT, or no additional liver-directed modality. A feedforward, ML ensemble model provided a treatment recommendation on the basis of pairwise assessments evaluating each potential treatment option and estimated benefit in survival. Results: Two hundred thirty-seven patients met inclusion criteria, of whom 54 (23%) and 49 (21%) received combination of TACE and SBRT or TACE and RFA, respectively. The ML model suggested a different consolidative modality in 32.7% of cases among patients who had previously received combination treatment. Patients treated in concordance with model recommendations had significant improvement in progression-free survival (hazard ratio 0.5; P = .007). The most important features for model prediction were cause of cirrhosis, stage of disease, and albumin-bilirubin grade (a measure of liver function). Conclusion: In this proof-of-concept study, an ensemble ML model was able to provide treatment recommendations for HCC who had undergone prior TACE. Additional treatment in line with model recommendations was associated with significant improvement in progression-free survival, suggesting a potential benefit for ML-guided medical decision making. Competing Interests: Nitin OhriConsulting or Advisory Role: Merck, GenentechResearch Funding: Merck (Inst), RefleXion Medical (Inst), AstraZeneca Shalom KalnickiTravel, Accommodations, Expenses: Elekta Chandan GuhaConsulting or Advisory Role: Johnson & Johnson, Varian Medical Systems, Focused Ultrasound FoundationResearch Funding: Celldex, Johnson & Johnson, Myelo Therapeutics GmbH (Inst)Patents, Royalties, Other Intellectual Property: Montefiore Medical CenterOpen Payments Link: https://openpaymentsdata.cms.gov/physician/480201 N. Patrik BrodinResearch Funding: Varian Medical Systems (Inst)No other potential conflicts of interest were reported.
Databáze:	MEDLINE
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