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Chokri Ben Lamine: Strategic Decision Making in Hematology under Competing Risks
Mar 30, 2026, 14:11

Chokri Ben Lamine: Strategic Decision Making in Hematology under Competing Risks

Chokri Ben Lamine, Adult Hematology and SCT Assistant Consultant at Oncology Center of Excellence at King Faisal Specialist Hospital and Research Center, shared a post on X:

“Game theory in Hematology equals strategic decision-making under competing risks (disease vs toxicity vs cost vs time).

Core idea: optimize outcome when multiple players (tumor, host, physician, system) interact․

AML induction vs wait

  • aggressive disease equals ‘move early’ (dominant strategy)
  • frail pt equals delay/low-intensity leads to minimize loss (minimax)․

MRD-guided decisions

  • MRD positive leads to escalate (allo-SCT)
  • MRD negative leads to de-escalate
  • dynamic strategy equals sequential game․

CAR-T vs bispecific vs SCT

  • competing strategies with different payoffs (CR, toxicity, durability)
  • choose based on patient fitness plus tumor biology leads to expected utility maximization․

GVHD vs GVL balance

  • immunosuppression decrease GVHD but increase relapse
  • optimal point equals Nash equilibrium between immune control and toxicity․

Anticoagulation in cancer thrombosis

  • bleeding risk vs clot risk
  • adjust dose/timing leads to risk-dominant strategy․

Transfusion thresholds (PBM)

  • liberal vs restrictive
  • restrictive often ‘dominant strategy’ leads to same outcomes, fewer risks․

Antibiotic use in FN

  • overuse leads to resistance
  • underuse leads to mortality
  • stewardship equals repeated game with system-level payoff․

Clonal evolution (AML/CLL/MM)

  • tumor adapts to therapy equals ‘evolutionary game’
  • combination/sequencing prevents resistant clone dominance․

Resource allocation (ICU beds, CAR-T slots)

  • prioritize pts with highest benefit probability
  • utility plus fairness balance․

One-liner

Hematology decisions equals multi-step strategic games leads to best outcomes when timing, sequencing, and risk trade-offs are optimized.”

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