6 research outputs found
Diagnostic Accuracy of Different Surgical Procedures for Axillary Staging After Neoadjuvant Systemic Therapy in Node-positive Breast Cancer
Objective: The aim of this study was to perform a systematic review and
meta-analysis to assess the accuracy of different surgical axillary staging
procedures compared with ALND.
Summary of Background Data: Optimal axillary staging after neoadjuvant
systemic therapy (NST) in node-positive breast cancer is an area of controversy. Several less invasive procedures, such as sentinel lymph node biopsy
(SLNB), marking axillary lymph node with radioactive iodine seed (MARI),
and targeted axillary dissection (a combination of SLNB and a MARI-like
procedure), have been proposed to replace the conventional axillary lymph
node dissection (ALND) with its concomitant morbidity.
Methods: PubMed and Embase were searched for studies comparing less
invasive surgical axillary staging procedures to ALND to identify axillary
burden after NSTin patients with pathologically confirmed node-positive breast
cancer (cNĂľ). A meta-analysis was performed to compare identification rate
(IFR), false-negative rate (FNR), and negative predictive value (NPV).
Results: Of 1132 records, 20 unique studies with 2217 patients were included
in quantitative analysis: 17 studies on SLNB, 1 study on MARI, and 2 studies
on a combination procedure. Overall axillary pathologic complete response
rate was 37%. For SLNB, pooled rates of IFR and FNR were 89% and 17%.
NPV ranged from 57% to 86%. For MARI, IFR was 97%, FNR 7%, and NPV
83%. For the combination procedure, IFR was 100%, FNR ranged from 2% to
4%, and NPV from 92% to 97%.
Conclusion: Axillary staging by a combination procedure consisting of
SLNB with excision of a pre-NST marked positive lymph node appears to
be most accurate for axillary staging after NST. More evidence from
prospective multicenter trials is needed to confirm this
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Overview of trials on artificial intelligence algorithms in breast cancer screening - A roadmap for international evaluation and implementation.
Accumulating evidence from retrospective studies demonstrate at least non-inferior performance when using AI algorithms with different strategies versus double-reading in mammography screening. In addition, AI algorithms for mammography screening can reduce work load by moving to single human reading. Prospective trials are essential to avoid unintended adverse consequences before incorporation of AI algorithms into UK's National Health Service (NHS) Breast Screening Programme (BSP). A stakeholders' meeting was organized in Newnham College, Cambridge, UK to undertake a review of the current evidence to enable consensus discussion on next steps required before implementation into a screening programme. It was concluded that a multicentre multivendor testing platform study with opt-out consent is preferred. AI thresholds from different vendors should be determined while maintaining non-inferior screening performance results, particularly ensuring recall rates are not increased. Automatic recall of cases using an agreed high sensitivity AI score versus automatic rule out with a low AI score set at a high sensitivity could be used. A human reader should still be involved in decision making with AI-only recalls requiring human arbitration. Standalone AI algorithms used without prompting maintain unbiased screening reading performance, but reading with prompts should be tested prospectively and ideally provided for arbitration
SEOM clinical guidelines in early stage breast cancer (2018)
Breast cancer is the most common cancer in women in our country and it is usually diagnosed in the early and potentially curable stages. Nevertheless, around 20-30% of patients will relapse despite appropriate locoregional and systemic therapies. A better knowledge of this disease is improving our ability to select the most appropriate therapy for each patient with a recent diagnosis of an early stage breast cancer, minimizing unnecessary toxicities and improving long-term efficacy