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Introduction of Staging Laparoscopy in the Management of Advanced Epithelial Ovarian, Tubal and Peritoneal Cancer

2014· article· en· W2332596163 on OpenAlexaboutno aff
Anna Fagotti, Giuseppe Vizzielli, Francesco Fanfani, Barbara Costantini, Gabriella Ferrandina, Valerio Gallotta, Salvatore Gueli Alletti, Lucia Tortorella, Giovanni Scambia

Bibliographic record

VenueObstetrical & Gynecological Survey · 2014
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDebulkingOvarian cancerLaparoscopySurgeryEpithelial ovarian cancerChemotherapyStage (stratigraphy)DiseaseOncologyCancerInternal medicine

Abstract

fetched live from OpenAlex

Most women with epithelial ovarian cancer (EOC) are diagnosed at advanced stages of disease (AEOC) when there is already widespread intraperitoneal disease. Survival rates are low for these women. The standard approach for AEOC patients is primary debulking surgery (PDS) followed by combination platinum-based chemotherapy. An option developed for these patients is use of neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS). A critical prognostic factor for survival in patients with AEOC who receive PDS or NACT/IDS is the amount of visible tumor after surgery. Survival rates are higher in patients with no or minimal residual disease (RT). The European Organisation for Research and Treatment of Cancer–National Cancer Institute of Canada Clinical Trials Group trial showed that NACT followed by IDS decreased postoperative morbidity and provided similar survival outcomes to those of PDS. To guide management of primary AEOC patients toward either PDS or NACT, staging laparoscopy (S-LPS) is proposed to assess the intra-abdominal dissemination of disease and to predict the chances of optimal cytoreduction, but the effect of this strategy on survival outcomes has not been evaluated. This retrospective study was designed to determine the prognostic impact of routine use of S-LPS in patients with primary AEOC on the progression-free survival and overall survival in women with AEOC. The study subjects were patients at a single tertiary referral center treated between 2006 and 2010; all submitted to S-LPS before treatment with PDS or NACT. Residual disease was classified as follows: RT = 0 (absence of grossly visible tumor [complete cytoreduction]) and RT of 1 cm or less (optimal cytoreduction). Cases were stratified based on residual tumor at the time of treatment (PDS or IDS). Univariate and multivariate analyses were used to assess the surgical and survival outcome. Staging laparoscopy was performed in 300 consecutive patients; no complications occurred related to the surgical procedure. Laparoscopic staging revealed a high tumor load in almost half of the patients (46.3%). After S-LPS, 49.3% of the women received an attempt of PDS; the remaining 50.7% were submitted to NACT. There was no significant difference between PDS and NACT in the percentages of complete (RT = 0) and optimal cytoreduction (RT ≤1 cm); the percentages of complete cytoreduction were 62.1% in the PDS group and 57.5% in the IDS group, and the percentages of optimal cytoreduction were 27.7% in the PDS group and 22.5% in the IDS group. Postoperative complications were lower in the NACT/IDS group than in the PDS group (P = 0.01). Women with complete cytoreduction at PDS had a median progression-free survival of 25 months (95% confidence interval, 15.1–34.8); this shows a significant survival advantage compared with all other patients, irrespective of the type of treatment used (P = 0.0001). Multivariate analysis showed that only RT (P = 0.011) and the performance status (P = 0.016) maintained an independent association with the progression-free survival. These data show that use of S-LPS for the management of AEOC at a single tertiary referral center was safe and did not have a negative impact on survival. Use of this approach may help individualize treatment and avoid unnecessary laparotomies and surgical complications.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.335

Codex and Gemma teacher scores by category

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

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 head0.022
GPT teacher head0.300
Teacher spread0.278 · 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 teacher head, 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

Citations2
Published2014
Admission routes1
Has abstractyes

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