ArticleJCO precision oncology2025
Imaging- and Tumor Biomarker-Based Multivariable Model for Early Prediction of Pathologic Complete Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer.
Article in JCO precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Beyond pCR Prediction: Subtype-Specific Artificial Intelligence for Treatment Tailoring in Breast Cancer Neoadjuvant Therapy.Cancers · 2026Review
- Development and verification of a prognostic nomogram model for triple-negative breast cancer patients with neoadjuvant therapy.Frontiers in oncology · 2026Article
- Early identification of neoadjuvant therapy non-response via multimodal immune-imaging biomarkers in breast cancer.Frontiers in immunology · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
28 authors.
Funding
Abstract
purposeThe response of triple-negative breast cancer (TNBC) to neoadjuvant therapy (NAT) varies widely. This study aimed to determine the performance of clinicopathologic biomarkers and volumetric changes on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting pathologic complete response (pCR) to NAT in patients with TNBC. PATIENTS AND
methodsThis study included 264 patients with stage I to III TNBC enrolled in a prospective clinical trial. These patients underwent DCE-MRI at baseline and after two and/or after four cycles of dose-dense anthracycline and cyclophosphamide. Tumor volume (TV) was calculated by measuring three tumor dimensions at each time point. Clinicopathologic markers were analyzed. Treatment response at surgery (pCR
resultsOf the 264 patients, 124 (47%) achieved a pCR. The optimal thresholds for TV reduction (TVR) on DCE-MRI were ≥60% after two cycles and ≥90% after four cycles. TVR, Ki-67, and stromal tumor-infiltrating lymphocytes (sTILs) were independently associated with pCR on univariable analysis. A combined model including TVR ≥60% after two cycles, sTILs, and Ki-67 predicted pCR with an AUC of 0.84 (90% CI. 0.76 to 0.92) in the discovery cohort and 0.80 (95% CI, 0.71 to 0.88) in the validation cohort. A combined model including TVR ≥90% after four cycles, sTILs, and Ki-67 predicted pCR with an AUC of 0.79 (90% CI, 0.71 to 0.86) in the discovery cohort and 0.80 (95% CI, 0.72 to 0.88) in the validation cohort.
conclusionA model incorporating imaging and clinicopathologic variables showed good performance in predicting pCR.
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