Trial reportJournal of clinical oncology : official journal of the American Society of Clinical Oncology2022
Intelligent Vacuum-Assisted Biopsy to Identify Breast Cancer Patients With Pathologic Complete Response (ypT0 and ypN0) After Neoadjuvant Systemic Treatment for Omission of Breast and Axillary Surgery.
Trial report in Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 42 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Can a Pathological Complete Response in the Breast be Diagnosed by Vacuum-assisted, Ultrasound Guided Minimal Invasive Biopsy After Neoadjuvant Chemotherapy in Breast Cancer Patients? A Proof of Concept From a Prospective Cohort Study
RESPONDER Trial - Diagnosis of Pathological Complete Response by Vacuum-assisted Biopsy After Neoadjuvant Chemotherapy in Breast Cancer
Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it, 66 citations in OpenAlex.
- Predictive value of pretreatment circulating inflammatory response markers in the neoadjuvant treatment of breast cancer: meta-analysis.The British journal of surgery · 2024Pooled it
- Review
- Beyond pCR Prediction: Subtype-Specific Artificial Intelligence for Treatment Tailoring in Breast Cancer Neoadjuvant Therapy.Cancers · 2026Review
- On-treatment core needle biopsy identifies early responders for axillary surgery de-escalation: results from a prospective cohort.British journal of cancer · 2026Article
- Predictive Value of Routine Imaging for the Diagnosis of Pathologic Complete Response After Neoadjuvant Chemotherapy in Breast Cancer.Annals of surgical oncology · 2026Article
- Artificial intelligence in breast surgery: Current applications, challenges, and future perspectives.The Journal of international medical research · 2026Review
- Diagnostic accuracy of modern imaging following neoadjuvant therapy for breast cancer: a comparative analysis of ultrasound versus [18F] FDG PET/MRI in breast response assessment.Frontiers in oncology · 2026Article
- Development and validation of a nomogram incorporating YpT stage for predicting ypN0 using multicenter data in clinically node-positive breast cancer.Scientific reports · 2025Article
- Progress in surgical management strategies after neoadjuvant therapy in breast cancer.Annals of medicine · 2025Review
- Multi-omics prediction of axillary treatment response and tumour microenvironment alterations in lymph node-positive luminal breast cancer.Cell death & disease · 2025Article
- Image-guided adaption of systemic therapy in breast cancer patients.European radiology · 2025Article
- Minimally invasive biopsy technique predicting breast pathological complete response after neoadjuvant therapy for breast cancer.Gland surgery · 2025Article
- Is It Possible to Accurately Evaluate the Tumor Bed After Neoadjuvant Chemotherapy Using a 14G Tru-Cut Biopsy?Life (Basel, Switzerland) · 2025Article
- MRI-based artificial intelligence models for post-neoadjuvant surgery personalization in breast cancer: a narrative review of evidence from Western Pacific.The Lancet regional health. Western Pacific · 2025Review
- Breast cancer: pathogenesis and treatments.Signal transduction and targeted therapy · 2025Review
- Synergistic Effects of Radiotherapy and PD‑1 Blockade in a Human‑Mimetic BRCAness Model of Triple-Negative Breast Cancer.International journal of biological sciences · 2025Article
- Machine Learning to Predict the Individual Risk of Treatment-Relevant Toxicity for Patients With Breast Cancer Undergoing Neoadjuvant Systemic Treatment.JCO clinical cancer informatics · 2024Article
- Utility of Axillary Staging in Older Patients with HER2-Positive Breast Cancer.Annals of surgical oncology · 2024Article
- Can multi-modal radiomics using pretreatment ultrasound and tomosynthesis predict response to neoadjuvant systemic treatment in breast cancer?European radiology · 2024Article
- Article
Corrections and comments
- Commented on by
Authors and funding
27 authors at 16 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeNeoadjuvant systemic treatment (NST) elicits a pathologic complete response in 40%-70% of women with breast cancer. These patients may not need surgery as all local tumor has already been eradicated by NST. However, nonsurgical approaches, including imaging or vacuum-assisted biopsy (VAB), were not able to accurately identify patients without residual cancer in the breast or axilla. We evaluated the feasibility of a machine learning algorithm (intelligent VAB) to identify exceptional responders to NST.
methodsWe trained, tested, and validated a machine learning algorithm using patient, imaging, tumor, and VAB variables to detect residual cancer after NST (ypT+ or in situ or ypN+) before surgery. We used data from 318 women with cT1-3, cN0 or +, human epidermal growth factor receptor 2-positive, triple-negative, or high-proliferative Luminal B-like breast cancer who underwent VAB before surgery (ClinicalTrials.gov identifier: NCT02948764, RESPONDER trial). We used 10-fold cross-validation to train and test the algorithm, which was then externally validated using data of an independent trial (ClinicalTrials.gov identifier: NCT02575612). We compared findings with the histopathologic evaluation of the surgical specimen. We considered false-negative rate (FNR) and specificity to be the main outcomes.
resultsIn the development set (n = 318) and external validation set (n = 45), the intelligent VAB showed an FNR of 0.0%-5.2%, a specificity of 37.5%-40.0%, and an area under the receiver operating characteristic curve of 0.91-0.92 to detect residual cancer (ypT+ or in situ or ypN+) after NST. Spiegelhalter's Z confirmed a well-calibrated model (
conclusionAn intelligent VAB algorithm can reliably exclude residual cancer after NST. The omission of breast and axillary surgery for these exceptional responders may be evaluated in future trials.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.