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A simulated trial with reinforcement learning for the efficacy of Irinotecan and Ifosfamide versus Topotecan in relapsed, extensive stage small cell lung cancer
by
Artaç, Mehmet
, Bozcuk, Hakan Şat
in
Age
/ Aged
/ Analysis
/ Anthracyclines
/ Antimitotic agents
/ Antineoplastic agents
/ Antineoplastic Combined Chemotherapy Protocols - therapeutic use
/ Artificial intelligence
/ Biomedical and Life Sciences
/ Biomedicine
/ Cancer
/ Cancer Research
/ Cancer therapies
/ Care and treatment
/ Chemotherapy
/ Chemotherapy, Combination
/ Clinical trials
/ Computer Simulation
/ Cyclophosphamide
/ Decision making
/ Diagnosis
/ Dosage and administration
/ Female
/ Health Promotion and Disease Prevention
/ Humans
/ Ifosfamide
/ Ifosfamide - administration & dosage
/ Ifosfamide - therapeutic use
/ Irinotecan
/ Irinotecan - administration & dosage
/ Irinotecan - therapeutic use
/ Lung cancer
/ Lung cancer, Small cell
/ Lung Neoplasms - drug therapy
/ Lung Neoplasms - mortality
/ Lung Neoplasms - pathology
/ Machine learning
/ Male
/ Medical prognosis
/ Medicine/Public Health
/ Metastasis
/ Methods
/ Middle Aged
/ Neoplasm Recurrence, Local - drug therapy
/ Neoplasm Staging
/ Oncology
/ Patient outcomes
/ Patients
/ Proportional Hazards Models
/ Python
/ Reinforcement learning
/ Relapsed disease
/ Simulated clinical trial
/ Simulation
/ Small cell lung cancer
/ Small cell lung carcinoma
/ Small Cell Lung Carcinoma - drug therapy
/ Small Cell Lung Carcinoma - mortality
/ Small Cell Lung Carcinoma - pathology
/ Statistical models
/ Surgical Oncology
/ Topotecan
/ Topotecan - administration & dosage
/ Topotecan - therapeutic use
/ Treatment Outcome
/ Vincristine
2024
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A simulated trial with reinforcement learning for the efficacy of Irinotecan and Ifosfamide versus Topotecan in relapsed, extensive stage small cell lung cancer
by
Artaç, Mehmet
, Bozcuk, Hakan Şat
in
Age
/ Aged
/ Analysis
/ Anthracyclines
/ Antimitotic agents
/ Antineoplastic agents
/ Antineoplastic Combined Chemotherapy Protocols - therapeutic use
/ Artificial intelligence
/ Biomedical and Life Sciences
/ Biomedicine
/ Cancer
/ Cancer Research
/ Cancer therapies
/ Care and treatment
/ Chemotherapy
/ Chemotherapy, Combination
/ Clinical trials
/ Computer Simulation
/ Cyclophosphamide
/ Decision making
/ Diagnosis
/ Dosage and administration
/ Female
/ Health Promotion and Disease Prevention
/ Humans
/ Ifosfamide
/ Ifosfamide - administration & dosage
/ Ifosfamide - therapeutic use
/ Irinotecan
/ Irinotecan - administration & dosage
/ Irinotecan - therapeutic use
/ Lung cancer
/ Lung cancer, Small cell
/ Lung Neoplasms - drug therapy
/ Lung Neoplasms - mortality
/ Lung Neoplasms - pathology
/ Machine learning
/ Male
/ Medical prognosis
/ Medicine/Public Health
/ Metastasis
/ Methods
/ Middle Aged
/ Neoplasm Recurrence, Local - drug therapy
/ Neoplasm Staging
/ Oncology
/ Patient outcomes
/ Patients
/ Proportional Hazards Models
/ Python
/ Reinforcement learning
/ Relapsed disease
/ Simulated clinical trial
/ Simulation
/ Small cell lung cancer
/ Small cell lung carcinoma
/ Small Cell Lung Carcinoma - drug therapy
/ Small Cell Lung Carcinoma - mortality
/ Small Cell Lung Carcinoma - pathology
/ Statistical models
/ Surgical Oncology
/ Topotecan
/ Topotecan - administration & dosage
/ Topotecan - therapeutic use
/ Treatment Outcome
/ Vincristine
2024
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A simulated trial with reinforcement learning for the efficacy of Irinotecan and Ifosfamide versus Topotecan in relapsed, extensive stage small cell lung cancer
by
Artaç, Mehmet
, Bozcuk, Hakan Şat
in
Age
/ Aged
/ Analysis
/ Anthracyclines
/ Antimitotic agents
/ Antineoplastic agents
/ Antineoplastic Combined Chemotherapy Protocols - therapeutic use
/ Artificial intelligence
/ Biomedical and Life Sciences
/ Biomedicine
/ Cancer
/ Cancer Research
/ Cancer therapies
/ Care and treatment
/ Chemotherapy
/ Chemotherapy, Combination
/ Clinical trials
/ Computer Simulation
/ Cyclophosphamide
/ Decision making
/ Diagnosis
/ Dosage and administration
/ Female
/ Health Promotion and Disease Prevention
/ Humans
/ Ifosfamide
/ Ifosfamide - administration & dosage
/ Ifosfamide - therapeutic use
/ Irinotecan
/ Irinotecan - administration & dosage
/ Irinotecan - therapeutic use
/ Lung cancer
/ Lung cancer, Small cell
/ Lung Neoplasms - drug therapy
/ Lung Neoplasms - mortality
/ Lung Neoplasms - pathology
/ Machine learning
/ Male
/ Medical prognosis
/ Medicine/Public Health
/ Metastasis
/ Methods
/ Middle Aged
/ Neoplasm Recurrence, Local - drug therapy
/ Neoplasm Staging
/ Oncology
/ Patient outcomes
/ Patients
/ Proportional Hazards Models
/ Python
/ Reinforcement learning
/ Relapsed disease
/ Simulated clinical trial
/ Simulation
/ Small cell lung cancer
/ Small cell lung carcinoma
/ Small Cell Lung Carcinoma - drug therapy
/ Small Cell Lung Carcinoma - mortality
/ Small Cell Lung Carcinoma - pathology
/ Statistical models
/ Surgical Oncology
/ Topotecan
/ Topotecan - administration & dosage
/ Topotecan - therapeutic use
/ Treatment Outcome
/ Vincristine
2024
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A simulated trial with reinforcement learning for the efficacy of Irinotecan and Ifosfamide versus Topotecan in relapsed, extensive stage small cell lung cancer
Journal Article
A simulated trial with reinforcement learning for the efficacy of Irinotecan and Ifosfamide versus Topotecan in relapsed, extensive stage small cell lung cancer
2024
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Overview
Objectives
Synthetic data may proxy clinical data. At the absence of direct clinical data, this study aimed to compare Irinotecan and Ifosfamide (II) with Topotecan in synthetic, recurrent small cell lung cancer (SCLC) patients within a simulated clinical trial.
Materials and methods
Two simulation stages were conducted. Initially, 200 recurrent SCLC cases were simulated to replicate a previous phase 3 trial, testing the utility of Cox proportional hazards model and simulation methodology together, where patients were randomized to receive Cyclophosphamide, Adriamycin, Vincristine (CAV) or Topotecan. In the second stage, 600 recurrent SCLC patients were simulated and randomized to compare Topotecan versus II in terms of overall survival (OAS), using Reinforcement Learning (RL) and Cox proportional hazards model.
Results
CAV versus Topotecan comparison showed no statistical difference in overall survival (hazard ratio (HR): 0.89, 95% CI: 0.67–1.18,
P
= 0.418), aligning with the original clinical trial. For the Topotecan versus II comparison, the RL framework significantly favored the II arm (mean reward points: 193.43 versus − 251.82, permutation
P
< 0.0001). Likewise, II arm exhibited superior median OAS compared to Topotecan arm (11.12 versus 6.30 months). HR was 0.44 (95% CI: 0.38–0.52) with
P
< 0.0001, in favor of II.
Conclusion
Artificial trial results for CAV versus Topotecan matched the original trial, confirming indifference of OAS. Additionally, II yielded superior overall survival compared to Topotecan in recurrent SCLC patients. These demonstrate the potential of RL and simulation in conjunction with Cox modelling for similar studies. However, definitive conclusions necessitate a randomized clinical trial between II and Topotecan in this patient cohort.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
Subject
/ Aged
/ Analysis
/ Antineoplastic Combined Chemotherapy Protocols - therapeutic use
/ Biomedical and Life Sciences
/ Cancer
/ Female
/ Health Promotion and Disease Prevention
/ Humans
/ Ifosfamide - administration & dosage
/ Ifosfamide - therapeutic use
/ Irinotecan - administration & dosage
/ Irinotecan - therapeutic use
/ Lung Neoplasms - drug therapy
/ Male
/ Methods
/ Neoplasm Recurrence, Local - drug therapy
/ Oncology
/ Patients
/ Python
/ Small Cell Lung Carcinoma - drug therapy
/ Small Cell Lung Carcinoma - mortality
/ Small Cell Lung Carcinoma - pathology
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