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Impact of lenvatinib (LEN) dose-intensity and starting dose on survival among patients with advanced hepatocellular carcinoma (HCC): Results from a Canadian multicenter database (HCC CHORD).

2021· article· en· W3171411687 on OpenAlexaffabout
Carla Pires Amaro, Michael J. Allen, Jennifer J. Knox, Erica S. Tsang, Howard J. Lim, Richard M. Lee‐Ying, Kelvin Chan, Jessica Qian, Brandon M. Meyers, Alia Thawer, Sulaiman Mohammed Saif Al-Saadi, Tina Hsu, Ravi Ramjeesingh, Hatim Karachiwala, Tasnima Abedin, Vincent C. Tam

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsBaker Hughes (Canada)Ottawa Regional Cancer FoundationDalhousie UniversityUniversity Health NetworkUniversity of TorontoJuravinski Cancer CentreSunnybrook Health Science CentreNova Scotia Cancer CentreOttawa HospitalPrincess Margaret Cancer CentreUniversity of Calgary
Fundersnot available
KeywordsMedicineHepatocellular carcinomaLenvatinibInternal medicineAdverse effectLiver cancerLiver diseaseProportional hazards modelGastroenterologyPortal vein thrombosisMilan criteriaResponse Evaluation Criteria in Solid TumorsHazard ratioOncologyProgressive diseaseSorafenibCirrhosisLiver transplantationDiseaseConfidence intervalTransplantation

Abstract

fetched live from OpenAlex

e16142 Background: The REFLECT trial established LEN as a first-line treatment option for HCC. However, decreased LEN exposure is common due to adverse events leading to dose reductions and treatment discontinuations. The aim of this study was to evaluate whether starting dose or dose-intensity of LEN affects survival. To our knowledge, this is the first study to examine dosing of LEN and survival in HCC patients treated outside of Asia. Methods: From July 2018 to December 2019, HCC patients treated with first-line LEN from 10 different Canadian cancer centers were included. Overall survival (OS), progression-free survival (PFS), disease control rate (DCR) and objective response rate (ORR) were retrospectively analyzed and compared across different mean dose-intensities (> 66.7% vs <=66.7%) and starting dose groups (Full vs reduced). Survival outcomes were assessed with Kaplan-Meier curves and Cox proportional hazards models. DCR and ORR were determined radiographically according to the treating physician´s assessment in clinical notes and not RECIST 1.1 or mRECIST. Results: A total of 173 patients were included. Median age was 67 years, 77% were men and 23% East Asian. The most frequent causes of liver disease were hepatitis C (38%) and B (20%). 56% of patients received localized treatment prior to LEN. Of those, 24% had TACE, 6% TARE and 8% had liver transplant. Before starting LEN 31% of patients were ECOG 0 and 57% were ECOG 1. Most patients were Child-Pugh A (81%) and BCLC stage C (73%). Main portal vein invasion was present in 15% of the patients. Median follow-up was 4.5 months. LEN was started at full dose in 54% of patients and 60% had a mean dose intensity greater than 66.7%. ORR, PFS and OS results and their comparison between the different starting dose and dose-intensities are shown in the table. In a multivariate model that adjusted for age, gender, stage, ECOG, Child-Pugh, BCLC, cirrhosis, liver etiology disease (hepatitis B, C and non-viral), presence of tumor thrombus, prior transplant and localized treatment, dose intensity (>66.7 vs <=66.7% [HR 0.70, 95% CI 0.42-1.18; p=0.18]) was not a predictor of survival. Conclusions: In HCC patients starting LEN at a reduced dose does not appear to compromise survival. LEN dose-intensity of > 66.7% was associated with improved survival, but not after controlling for potential confounders.[Table: see text]

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.378

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.114
GPT teacher head0.368
Teacher spread0.255 · 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 designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2021
Admission routes2
Has abstractyes

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