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Extended criteria liver resection: Can noncurative intent resection of colorectal liver metastasis improve survival?

2023· article· en· W4318904012 on OpenAlexaff
Jennifer Kalil, Lucyna Krzywoń, O. Zlotnik, Prosanto Chaudhury, Anthoula Lazaris, Peter Metrakos

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsMcGill University Health CentreMcGill University
Fundersnot available
KeywordsMedicineDebulkingTumor DebulkingSurgeryAblative caseStage (stratigraphy)ChemotherapyHepatectomyMetastasisResectionCancerInternal medicineRadiation therapyOvarian cancer

Abstract

fetched live from OpenAlex

164 Background: From initially believed to be a palliative diagnosis, CRLM management has ranged from nonoperative, resection of only solitary lesions, resection only if 1 cm margin could be obtained, to major hepatectomies with liver regenerative procedures. With a greater understanding of the disease and advances in systemic therapy, clinicians have been able to continuously push the envelope in both resection and local therapies to improve overall survival in patients with CRLM. The constant evolution of management for CRLM raises the question – would patients benefit from a non-curative intent resection by decreasing overall tumor burden? Is there a role for debulking of disease as an extended criterion to improve median survival? Methods: A retrospective study was conducted by identifying patients who consented to participate in the liver disease biobank research program. Consented patients with CRLM evaluated for a two-stage hepatectomy (TSH) between 2012-2020 were identified. Patients were divided into three groups: those that successfully completed TSH, those that underwent the first stage only of the TSH (surgical debulking + best chemotherapy +/- local ablative therapy), and those that were not resected (best chemotherapy +/- local ablative therapy). Patients that underwent successful completion of TSH were excluded from analysis. Kaplan Meier survival curves and log-rank test were performed to assess the median survival between the surgical debulking + best chemotherapy +/- local ablative therapy and best chemotherapy +/- local ablative therapy groups. Results: Of the 114 patients identified, 47 patients underwent successful completion of TSH, 35 patients underwent the first stage only of the TSH, and 32 patients were eventually deemed unresectable. Reasons for unresectability included intraoperative findings (n = 14), progression of disease on best chemotherapy (n = 8), unresectable extrahepatic disease, local recurrence of primary, or unfit for surgery. Patients who underwent first stage of the TSH and those that were not resected both received best chemotherapy with/without local ablative therapies. 32 of the 35 patients in the surgical debulking + best chemotherapy +/- local ablative therapy group and 27 of 32 patients in the best chemotherapy +/- local ablative therapy group presented with synchronous disease. Tumor volume resected ranged from 5% - 95%. Median survial of the surgical debulking + best chemotherapy +/- local ablative therapy group was 31 months vs 20 months in the best chemotherapy +/- local ablative therapy group (p = 0.029). Conclusions: Patients undergoing incomplete resection of CRLM demonstrated a survival benefit compared to patients who did not undergo any surgical resection. This suggests there may be a potential systemic benefit to reducing overall tumor burden. This preliminary study provides the framework to further explore the systemic effects of surgical debulking of CRLM to improve median survival as an extended criterion for non-curative intent liver resection.

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)0.003
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.320
GPT teacher head0.457
Teacher spread0.137 · 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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