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Abstract S5-04: A large prospectively-designed study of the DCIS score: Predicting recurrence risk after local excision for ductal carcinoma in situ patients with and without irradiation

2015· article· en· W1638065039 on OpenAlexaffabout
Eileen Rakovitch, Sharon Nofech‐Mozes, Wedad Hanna, Frederick L. Baehner, Refik Saskin, Steven M. Butler, Alan B. Tuck, Sandip Sengupta, Leela Elavathil, Prashant Jani, M. Bonin, Martin C. Chang, Elzbieta Slodkowska, Joseph M Anderson, Farid Jamshidian, Diana B. Cherbavaz, Steven Shak, Lawerence Paszat

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsNOSM UniversityKingston General HospitalThunder Bay Regional Health Sciences CentreHealth Sciences CentreLondon Health Sciences CentreHealth Sciences NorthUniversity of TorontoInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineDuctal carcinomaHazard ratioBreast-conserving surgeryInternal medicineCohortPopulationOncologyBreast cancerProportional hazards modelRadiation therapyUrologyMastectomyGynecologyCancerConfidence interval

Abstract

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Abstract Background: DCIS patients need better tools to align the aggressiveness of treatment with the aggressiveness of their disease. The DCIS Score (DS) was validated as a predictor of ipsilateral breast recurrence (IBR; DCIS or invasive) in 327 E5194 patients treated by breast-conserving surgery (BCS) without radiation (RT) (Solin,2013). This Ontario population based DCIS study of 3335 women with DCIS from 1994 to 2003 (Rakovitch,2013) was conducted to test the DCIS Score as a predictor of recurrence risk in patients treated with BCS alone and in patients treated with BCS+RT. Methods: REMARK guidelines were followed. Breast pathologists centrally reviewed all H&E slides. The Oncotype DX DCIS Score was obtained by standardized quantitative RT-PCR using fixed paraffin embedded tumor. The pre-specified primary objective was to determine the relationship (hazard ratio (HR)/50 units) between the risk of an IBR and the continuous DS (using Cox models) in patients treated with BCS alone with ER+ tumors and clear margins (CM, no ink on tumor). Results: Tumor blocks were collected for 1569 patients (47% of parent cohort); 718 received BCS without RT (N=571 with CM) and 846 received BCS+RT (N=689 with CM). Median follow-up was 9.4 years. Among 1260 pts with CM, 100 pts treated with BCS alone had an IBR (DCIS, N=44; invasive, N=57); 86 pts treated with BCS+RT had an IBR (DCIS, N=32; invasive, N=55). In the primary analysis, among 571 patients treated by BCS alone with CM the continuous DS was significantly associated with IBR in ER+ patients (HR 2.26; 95%CI 1.41,3.59; P=0.001) and in all patients (HR 2.15; 95%CI 1.43,3.22; P=<0.001). The DS was also associated with invasive IBR (HR 1.78; 95%CI 1.03,3.05; P=0.04); similar but non-significant results were noted in the ER+ subgroup (P=0.08). Among 689 pts with CM treated by BCS+RT, the DS was associated with IBR (HR 2.78; 95%CI 1.77,4.41; P=<0.001). There was no interaction between the DS and RT (P=0.40). In multivariable analysis for IBR in CM cases, the HR/50 units for the DCIS Score among patients treated with BCS alone was 1.80(95%CI 1.17,2.74; P=0.008) and 2.86(95%CI 1.79,4.62, P=<0.001) for those treated with BCS+RT adjusting for multifocality, tumor size and age. Conclusions: DCIS Score quantifies recurrence risk for DCIS patients treated by BCS with or without RT. Integrating the DCIS Score with established risk factors, such as multifocality, age, and tumor size, can help identify DCIS patients treated with BCS alone with low 10 year risk (<10%) of recurrence and those who still have high 10 year risk of recurrence despite RT who may be candidates for more aggressive treatment. BCS Alone, CMBCS with RT, CMDCIS Score Risk Group10-Yr Kaplan-Meier IBR Rate (95%CI)10-Yr Kaplan-Meier IBR Rate (95%CI) All patients (N=571)Unifocal DCIS (N=457)All patients (N=689)Multifocal DCIS (N=177)Low (<39)12.7% (9.5%,16.9%) N=3559.7% (6.8%,13.8%) N=2987.5% (4.9%,11.2%) N=3329.8% (4.6%,20.1%) N=91Int (39-54)33.0% (23.6%,44.8%) N=9527.1% (17.7%,40.2%) N=7213.6% (8.6%,21.2%) N=15520.6% (10.3%,38.7%) N=37High (>55)27.8% (20.0%,37.8%) N=12127.0% (18.2%,38.9%) N=8720.5% (15.1%,27.5%) N=20233.3% (21.9%,48.5%) N=49Log rank P-value<0.001<0.001<0.001<0.001 Citation Format: Eileen Rakovitch, Sharon Nofech-Mozes, Wedad Hanna, Frederick L Baehner, Refik Saskin, Steven M Butler, Alan Tuck, Sandip Sengupta, Leela Elavathil, Prashant A Jani, Michel Bonin, Martin C Chang, Elzbieta Slodkowska, Joseph M Anderson, Farid Jamshidian, Diana B Cherbavaz, Steven Shak, Lawerence Paszat. A large prospectively-designed study of the DCIS score: Predicting recurrence risk after local excision for ductal carcinoma in situ patients with and without irradiation [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr S5-04.

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)0.001
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.035
GPT teacher head0.338
Teacher spread0.303 · 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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Citations9
Published2015
Admission routes2
Has abstractyes

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