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Record W2791438625 · doi:10.1007/s11864-018-0532-2

When to Stop Tyrosine Kinase Inhibitors for the Treatment of Chronic Myeloid Leukemia

2018· review· en· W2791438625 on OpenAlexaff
Pierre Laneuville

Bibliographic record

VenueCurrent Treatment Options in Oncology · 2018
Typereview
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineNilotinibChronic myelogenous leukemiaDasatinibOncologyInternal medicineTyrosine-kinase inhibitorImatinib mesylateMyeloid leukemiaClinical trialClinical endpointIntensive care medicineImatinibLeukemiaCancer

Abstract

fetched live from OpenAlex

The outcome of chronic myelogenous leukemia (CML) patients presenting in the chronic phase has changed dramatically since the introduction of tyrosine kinase inhibitor (TKI) therapy with imatinib in 2001 and second-generation TKIs in 2007. With the availability of TKI therapy, standardized molecular monitoring, and the adoption of response-adapted intervention for patients who “fail” to respond adequately, as defined in evidence-based international guidelines[ 1 , 2 ], patients with this historically fatal disease now have survival approaching that of the normal population [ 3 ]. While it was initially believed that TKI therapy would need to be continued indefinitely, it is now well accepted that a subgroup of patients who achieve a deep and sustained molecular response (DMR) can successfully discontinue TKI therapy and maintain a treatment-free remission (TFR). This was first demonstrated in the STIM1 trial [ 4 ] following demonstrated feasibility in a smaller study (STIM-Pilot) [ 5 ]. Discontinuation of first-line imatinib in patients who maintained a state of undetectable molecular residual disease (UMRD) for at least 2 years, measured by quantitative real-time reverse transcriptase polymerase chain reaction (qRT-PCR) with a sensitivity of 0.0032% (− 4.5 logs) on the International Scale (IS), UMRD4.5, the molecular relapse-free survival after 60 months was 36% [ 4 ]. This sets a precedent for a growing list of TKI discontinuation trials with minimal criteria for the necessary DMR to achieve varying from major molecular response (MMR) to UMRD5.0, sustained for a minimum of 1 to 2 years, and different criteria for the reinstatement of treatment ranging from molecular relapse to the loss of MMR. Variable rates of TFR have been reported with the majority falling in the range of 40 to 60% with success or failure occurring in the first 6 months in the majority of patients. The rate of TFR is strongly influenced by how it is defined as first shown in the A-STIM trial where the estimated rate of TFR increased from 46 to 64% at 2 years using STIM1 versus the loss of MMR as criteria [ 6 ]. Results from “real-world” studies are in general agreement with those from prospective clinical trials and have confirmed the importance of maintaining MR4.0 for at least 2 years to ensure a reasonable chance of success. Discontinuation of TKI therapy in the clinical trial setting appears to be safe with the majority of patients who fail to maintain a TFR regaining a DMR after a few months of restarting TKI therapy. Only a single patient has died to date after transforming to advanced phase disease in more than 2500 patients reported. The heterogeneity of trial criteria and results raises several challenges to define criteria for when it is appropriate and safe to stop TKI therapy in general clinical practice. This review highlights some of these challenges.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.107
GPT teacher head0.421
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations35
Published2018
Admission routes1
Has abstractyes

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