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Record W4392641667 · doi:10.1055/s-0044-1781403

Pneumologische Onkologie Tumor Treating Fields (TTFields) therapy with standard systemic therapy in metastatic non-small cell lung cancer following progression on or after platinum-based therapy: global randomized, pivotal (phase 3) LUNAR study

2024· article· en· W4392641667 on OpenAlexaff
Richard Greil, Ticiana Leal, Rupesh Kotecha, Rodryg Ramlau, Li Zhang, Janusz Milanowski, Manuel Cobo, J Roubec, Luboš Petruželka, Libor Havel, Sujith Kalmadi, John H. Ward, Zoran Andrić, Thierry Berghmans, David E. Gerber, Goetz Kloecker, Rajiv Panikkar, Joachim G.J.V. Aerts, Angelo Delmonte, Miklos Pless, Christian Rolfo, Matthew D. Eaton, M.S. Iqbal, Wallace Akerley, Corey J. Langer

Bibliographic record

VenuePneumologie · 2024
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineSystemic therapyLung cancerOncologyCancerRandomized controlled trialInternal medicineBreast cancer

Abstract

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Background : TTFields are electric fields that disrupt cancer cell viability. TTFields therapy is approved for glioblastoma and mesothelioma. The randomized, pivotal (phase 3) LUNAR study (NCT02973789) assessed the efficacy and safety of TTFields therapy with investigator’s choice of standard systemic therapy (ST; immune checkpoint inhibitor [ICI] or docetaxel [DTX], standard of care at time of study design) for metastatic non-small cell lung cancer (mNSCLC) progressing on/after platinum-based therapy. Methods : Adult patients were randomized 1:1 to TTFields+ST or ST. Primary endpoint: overall survival (OS); key secondary endpoints: OS in ICI and DTX subgroups; other secondary endpoints: adverse events (AEs) and patient-reported HRQoL assessed from baseline to 54 weeks by the validated EORTC QLQ-C30 questionnaire. Results: 276 patients (TTFields+ST, n=137; ST, n=139) were included. Median (m) age was 64 years (range, 22– 86), 64% were male, 57% had non-squamous NSCLC, 96% had ECOG PS 0–1, 10% had >1 prior line of ST, and 32% had prior ICI. Characteristics were balanced between arms. OS was significantly extended with TTFields+ST vs ST: mOS (95% CI) 13.2 (10.3–15.5) vs 9.9 (8.1–11.5) months (mo); HR 0.74 (95% CI 0.56–0.98); P=0.035. In the ICI subgroup (n=134), TTFields therapy significantly improved OS vs ICI alone: mOS (95% CI) 18.5 (10.6–30.3) vs 10.8 (8.2–18.4) mo; HR 0.63 (95% CI 0.41–0.96); P=0.030. In the DTX subgroup (n=142), mOS (95% CI) with TTFields+DTX vs DTX was 11.1 (8.2–14.1) vs 8.7 (6.3–11.3) mo; HR 0.81 (95% CI 0.55–1.19); P=0.28. AEs were similar for TTFields+ST (97%) vs ST (91%). 71% reported a device-related AE; most were dermatological, and 6% were grade 3. For global health status, there was no clinically meaningful (≥10 points) decline in either treatment group, or difference between treatment groups. Discussion: TTFields therapy plus ST extended OS vs ST without exacerbating systemic toxicities or adversely affecting HRQoL in patients with mNSCLC progressing on/after platinum-based therapy. Conclusion: These results warrant TTFields therapy as an option to manage mNSCLC in this setting. Publication History Article published online: 01 March 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag Rüdigerstraße 14, 70469 Stuttgart, Germany

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.389
Teacher spread0.359 · 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 designRandomized trial
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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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