Details

Autor(en) / Beteiligte

• Endo, Yutaka
• Alaimo, Laura
• Moazzam, Zorays
• Woldesenbet, Selamawit
• Lima, Henrique A.
• Yang, Jason
• Munir, Muhammad Musaab
• Shaikh, Chanza F.
• Azap, Lovette
• Katayama, Erryk
• Rueda, Belisario Ortiz
• Guglielmi, Alfredo
• Ruzzenente, Andrea
• Aldrighetti, Luca
• Alexandrescu, Sorin
• Kitago, Minoru
• Poultsides, George
• Sasaki, Kazunari
• Aucejo, Federico
• Pawlik, Timothy M.

Titel

Optimal policy tree to assist in adjuvant therapy decision-making after resection of colorectal liver metastases

Ist Teil von

• Surgery, 2024-03, Vol.175 (3), p.645-653

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2024

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• Although systemic postoperative therapy after surgery for colorectal liver metastases is generally recommended, the benefit of adjuvant chemotherapy has been debated. We used machine learning to develop a decision tree and define which patients may benefit from adjuvant chemotherapy after hepatectomy for colorectal liver metastases. Patients who underwent curative-intent resection for colorectal liver metastases between 2000 and 2020 were identified from an international multi-institutional database. An optimal policy tree analysis was used to determine the optimal assignment of the adjuvant chemotherapy to subgroups of patients for overall survival and recurrence-free survival. Among 1,358 patients who underwent curative-intent resection of colorectal liver metastases, 1,032 (76.0%) received adjuvant chemotherapy. After a median follow-up of 28.7 months (interquartile range 13.7–52.0), 5-year overall survival was 67.5%, and 3-year recurrence-free survival was 52.6%, respectively. Adjuvant chemotherapy was associated with better recurrence-free survival (3-year recurrence-free survival: adjuvant chemotherapy, 54.4% vs no adjuvant chemotherapy, 46.8%; P < .001) but no overall survival significant improvement (5-year overall survival: adjuvant chemotherapy, 68.1% vs no adjuvant chemotherapy, 65.7%; P = .15). Patients were randomly allocated into 2 cohorts (training data set, n = 679, testing data set, n = 679). The random forest model demonstrated good performance in predicting counterfactual probabilities of death and recurrence relative to receipt of adjuvant chemotherapy. According to the optimal policy tree, patient demographics, secondary tumor characteristics, and primary tumor characteristics defined the subpopulation that would benefit from adjuvant chemotherapy. A novel artificial intelligence methodology based on patient, primary tumor, and treatment characteristics may help clinicians tailor adjuvant chemotherapy recommendations after colorectal liver metastases resection.