Modelling Predictors of Molecular Response to Frontline Imatinib for Patients with Chronic Myeloid Leukaemia

Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?

Artificial intelligence (AI) is emerging as a discipline capable of providing significant added value in Medicine, in particular in radiomic, imaging analysis, big dataset analysis, and also for generating virtual cohort of patients. However, in coping with chronic myeloid leukemia (CML), considered an easily managed malignancy after the introduction of TKIs which strongly improved the life expectancy of patients, AI is still in its infancy. Noteworthy, the findings of initial trials are intriguing and encouraging, both in terms of performance and adaptability to different contexts in which AI can be applied. Indeed, the improvement of diagnosis and prognosis by leveraging biochemical, biomolecular, imaging, and clinical data can be crucial for the implementation of the personalized medicine paradigm or the streamlining of procedures and services. In this review, we present the state of the art of AI applications in the field of CML, describing the techniques and objectives, and with a general focus that goes beyond Machine Learning (ML), but instead embraces the wider AI field. The present scooping review spans on publications reported in Pubmed from 2003 to 2023, and resulting by searching "chronic myeloid leukemia" and "artificial intelligence". The time frame reflects the real literature production and was not restricted. We also take the opportunity for discussing the main pitfalls and key points to which AI must respond, especially considering the critical role of the 'human' factor, which remains key in this domain.

Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 1-Overview of Knowledge Discovery Techniques in Artificial Intelligence

Aim

To perform a systematic review on the application of artificial intelligence (AI) based knowledge discovery techniques in pharmacoepidemiology.

Study Eligibility Criteria

Clinical trials, meta-analyses, narrative/systematic review, and observational studies using (or mentioning articles using) artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded.

Data Sources

Articles recorded from 1950/01/01 to 2019/05/06 in Ovid MEDLINE were screened.

Participants

Studies including humans (real or simulated) exposed to a drug.

Results

In total, 72 original articles and 5 reviews were identified via Ovid MEDLINE. Twenty different knowledge discovery methods were identified, mainly from the area of machine learning (66/72; 91.7%). Classification/regression (44/72; 61.1%), classification/regression + model optimization (13/72; 18.0%), and classification/regression + features selection (12/72; 16.7%) were the three most frequent tasks in reviewed literature that machine learning methods has been applied to solve. The top three used techniques were artificial neural networks, random forest, and support vector machines models.

Conclusions

The use of knowledge discovery techniques of artificial intelligence techniques has increased exponentially over the years covering numerous sub-topics of pharmacoepidemiology.

Systematic Review Registration

Systematic review registration number in PROSPERO: CRD42019136552.

The impact of introducing tyrosine kinase inhibitors on chronic myeloid leukemia survival: a population-based study

BACKGROUND

Chronic myeloid leukemia is associated with a BCR/ABL oncoprotein inhibited by imatinib mesylate, the first tyrosine kinase inhibitor. Although experimental studies have clearly demonstrated the efficacy of imatinib, up-to-date data on its effectiveness at the population level are limited. Our study aims to assess the change in disease-specific survival for chronic myeloid leukemia after introducing tyrosine kinase inhibitors in first-line treatment.

METHODS

This study analyzed data from two population-based cancer registries in Italy. Disease-specific survival for chronic myeloid leukemia cases diagnosed before and after the introduction of tyrosine kinase inhibitors (February 2002) were calculated up to 10 years. Hazard ratios were calculated using Cox regression models adjusted for sex, age at diagnosis and residency. An interrupted time series analysis was also performed.

RESULTS

Between 1996 and 2012, 357 new cases of chronic myeloid leukemia were diagnosed (standardized incidence rate of 1.2 per 100,000 residents), quite constant throughout the period. The interrupted time series analysis showed a gain of 40.4% in 5 years of disease-specific survival for chronic myeloid leukemia (from 47.3, 95%CI 38.5-55.5% to 80.8%, 95%CI 74.5-85.8%) after the introduction of tyrosine kinase inhibitors. The hazard ratio was 0.36 (95%CI 0.25-0.52) for cases diagnosed after tyrosine kinase inhibitor introduction, with differences per age at diagnosis: <65yo 0.17 (95%CI 0.08-0.39), >74yo 0.41 (95%CI 0.23-0.73). An improvement in survival (hazard ratio 0.66, 95%CI 0.36-1.20) was also observed in cases diagnosed before, and alive at, tyrosine kinase inhibitors introduction.

CONCLUSIONS

Tyrosine kinase inhibitors increased disease-specific survival both for new and prevalent chronic myeloid leukemia cases. The effectiveness was similar to that observed in trials only in patients ages 65 years or younger.