MétaCan
Menu
← Back to cohort

Association of MRI biomarkers on survival in TNBC patients: A retrospective, multi-center cohort study.

2025· article· en· W4410802702 on OpenAlexaffabout
Nima Toussi, Ambika Chandrasekhar, Prosanta Mondal, Madhumita Manna

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineRetrospective cohort studyCohortOncologyCenter (category theory)Internal medicine

Abstract

fetched live from OpenAlex

e13155 Background: Triple-negative breast cancer (TNBC) is an aggressive subtype associated with heterogenous radiological and histopathological findings. Emerging MRI-based biomarkers—such as peritumoral edema and rim enhancement—may better predict disease progression and recurrence. However, multi-center evidence remains limited, warranting further investigation. Methods: In this retrospective study, women diagnosed with triple-negative breast cancer (TNBC) in the Canadian province of Saskatchewan from 2017 to 2021 were identified. Radiologist MRI reports were independently evaluated and coded to document the presence of peritumoral edema and rim enhancement. Multifactorial data on patient demographics, treatment, and staging were also collected. The relationship between these MRI features and overall survival was analyzed by a non-parametric survival analysis and Cox regression analyses. Chi-squared tests were conducted to evaluate differences in key clinical and pathological outcomes among patients with or without peritumoral edema and rim enhancement. Results: 380 patients with TNBC were included in the study, of which 174 received a breast MRI within 6 months of their diagnosis. Of these patients, 38 (10.0%) had peritumoral edema pre-systemic therapy, and 44 (11.5%) had rim enhancement pre-systemic therapy. The median overall survival for those with peritumoral edema was 1.88 years (95% CI, 1.29 - 6.15) and 2.47 years (95% CI, 1.35 – 6.15) for those with rim enhancement, as compared to 5.10 years (95% CI, 4.30-6.79) for the entire study population. Multivariate analysis demonstrated that peritumoral edema (p = 0.36, HR [95% CI] = 1.53 [0.61 –3.81] and rim enhancement (p = 0.21, HR [95% CI] = 1.81 [0.71 – 4.63] were not significant predictors of overall survival. Those with peritumoral edema were more likely to undergo first-line dose reduction in systemic therapy (p = 0.01, OR [95% CI] = 2.96 [1.25 –7.04]), though this finding was not significant for those with rim enhancement (p = 0.12, OR [95% CI] = 2.08 [0.83 –5.23]). Amongst patients with post-resection pathology post-neoadjuvant systemic therapy, those with rim enhancement and/or peritumoral edema exhibited a greater rate of residual disease versus complete pathological response, though this result was statistically non-significant (p = 0.12, OR [95% CI] = 2.13 [0.83 –5.43]). Similar results were observed with tumor necrosis on post-resection pathology post-neoadjuvant chemotherapy (p = 0.23, OR [95% CI] = 2.03 [0.65 –6.38]). Conclusions: The presence of peritumoral edema and rim enhancement were associated with poor survival outcomes in TNBC. However, these findings are not definitively extended to treatment response, treatment course and pathological outcomes. Larger datasets are required to better elucidate a possible connection between MRI-biomarkers and post-treatment pathology and prognosis.

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 score0.063
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.446
Teacher spread0.412 · 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
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Clinical Oncology→Same topicRadiomics and Machine Learning in Medical Imaging→French-language works237,207→