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MP14-18 AN ANATOMIC CLASSIFICATION SYSTEM FOR LOCAL RECURRENCE FOLLOWING RESECTION OF INTERMEDIATE AND HIGH RISK NON-METASTATIC RENAL CELL CARCINOMA: AN ANALYSIS OF THE ASSURE (ECOG-ACRIN 2805) TRIAL

2019· article· en· W2941284207 on OpenAlexaboutno aff
Ziho Lee, Opeyemi A. Jegede, Naomi B. Haas, Michael R. Pins, Edward M. Messing, Judith Manola, Christopher G. Wood, Christopher J. Kane, Michael A.S. Jewett, Keith T. Flaherty, Janice P. Dutcher, Robert S. DiPaola, Robert G. Uzzo

Bibliographic record

VenueThe Journal of Urology · 2019
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRenal cell carcinomaOncologyResectionSurgeryInternal medicine

Abstract

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You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging/Surveillance II (MP14)1 Apr 2019MP14-18 AN ANATOMIC CLASSIFICATION SYSTEM FOR LOCAL RECURRENCE FOLLOWING RESECTION OF INTERMEDIATE AND HIGH RISK NON-METASTATIC RENAL CELL CARCINOMA: AN ANALYSIS OF THE ASSURE (ECOG-ACRIN 2805) TRIAL Ziho Lee*, Opeyemi Jegede, Naomi B. Haas, Michael R. Pins, Edward M. Messing, Judith Manola, Christopher G. Wood, Christopher J. Kane, Michael A.S. Jewett, Keith T. Flaherty, Janice P. Dutcher, Robert S. DiPaola, and Robert G. Uzzo Ziho Lee*Ziho Lee* More articles by this author , Opeyemi JegedeOpeyemi Jegede More articles by this author , Naomi B. HaasNaomi B. Haas More articles by this author , Michael R. PinsMichael R. Pins More articles by this author , Edward M. MessingEdward M. Messing More articles by this author , Judith ManolaJudith Manola More articles by this author , Christopher G. WoodChristopher G. Wood More articles by this author , Christopher J. KaneChristopher J. Kane More articles by this author , Michael A.S. JewettMichael A.S. Jewett More articles by this author , Keith T. FlahertyKeith T. Flaherty More articles by this author , Janice P. DutcherJanice P. Dutcher More articles by this author , Robert S. DiPaolaRobert S. DiPaola More articles by this author , and Robert G. UzzoRobert G. Uzzo More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555314.30838.23AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Local recurrence (LR) after extirpative surgery for renal cell carcinoma (RCC) refers to cancer that has recurred at or near the site of the primary tumor. Currently, no existing definition of LR accounts for differences in location or extent of recurrence, and the potential prognostic implications of these differences are unknown. We describe a novel anatomic classification system for LR, and evaluate clinicopathologic risk factors associated with potential survival differences based on LR type. METHODS: Recurrence data from the ASSURE (ECOG-ACRIN 2805) trial were queried for all patients with fully resected intermediate or high risk non-metastatic RCC with LR. All patients with concurrent metastasis (any extra-abdominal recurrence) at time of LR were excluded. The cohort was divided into four LR groups: Type I: remnant organ or adjacent soft tissue recurrence; Type II: ipsilateral vein (vein remnant or IVC), gland (adrenal) or node recurrence; Type III: distant intra-abdominal soft tissue or visceral recurrence; and Type IV: any combination of Types 1-3 LR. Multivariable logistic regression was used to identify clinicopathologic predictors. The covariates assessed included age, gender, postoperative performance status, AJCC stage, Fuhrman grade (FG), sarcomatoid differentiation, positive surgical margin, papillary pathology, and use of minimally-invasive surgery. Logrank test was used to compare RCC-specific survival (RCCS) and overall survival (OS). RESULTS: Of 1,943 patients in ASSURE, 300 (15.4%) patients had LR. There were 65 (21.7%) Type 1, 99 (33.0%) Type 2, 93 (31.0%) Type 3, and 43 (14.3%) Type 4 LR patients. On multivariable analysis, use of minimally-invasive surgery did not predict any type of LR. Type I LR was predicted by higher AJCC stage (p=0.001) and FG (p=0.034), and papillary pathology (p=0.039). Type II LR was predicted by higher AJCC stage (p<0.001) and FG (p=0.001). Worse postoperative performance status (p=0.032) was the only variable associated with Type III LR. Type IV LR was predicted by male gender (p=0.014), and higher AJCC stage (p<0.001) and FG (p=0.003). Five-year RCCS (p<0.001) and OS (p<0.001) were worse for patients with Type 4 LR (median 37.1% and 31.2%, respectively) compared to those with Types 1-3 LR (median 61.5-71.7% and 57.9-65.7%, respectively). There was no difference in 5-year RCCS and OS among Types 1-3 LR. CONCLUSIONS: Our anatomic classification system may be used to categorize LR based on location and extent of tumor burden. Although there was no difference in survival when LR was limited to a single anatomic subdivision, LR that involved multiple subdivisions (Type 4) was associated with worse survival. Source of Funding: This study was supported by the National Cancer Institute of the National Institutes of Health under the following award numbers: CA180820, CA180794, CA180867, CA180858, CA180888, CA180821, CA180863, and Canadian Cancer Society #704970. Philadelphia, PA; Boston, MA; Philadelphia, PA; Park Ridge, IL; Rochester, NY; Boston, MA; Houston, TX; San Diego, CA; Toronto, Canada; Boston, MA; New York, NY; Lexington, KY; Philadephia, PA© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e194-e194 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ziho Lee* More articles by this author Opeyemi Jegede More articles by this author Naomi B. Haas More articles by this author Michael R. Pins More articles by this author Edward M. Messing More articles by this author Judith Manola More articles by this author Christopher G. Wood More articles by this author Christopher J. Kane More articles by this author Michael A.S. Jewett More articles by this author Keith T. Flaherty More articles by this author Janice P. Dutcher More articles by this author Robert S. DiPaola More articles by this author Robert G. Uzzo More articles by this author Expand All Advertisement PDF downloadLoading ...

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.285
Teacher spread0.261 · 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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Published2019
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