In patients with operable early breast cancer, neoadjuvant systemic treatment (NST) is a standard approach. Indications have expanded from downstaging of locally advanced breast cancer to facilitate breast conservation, to in vivo drug-sensitivity testing. The pattern of response to NST is used to tailor systemic and locoregional treatment, that is, to escalate treatment in nonresponders and de-escalate treatment in responders. Here we discuss four questions that guide our current thinking about 'response-adjusted' surgery of the breast after NST. (i) What critical diagnostic outcome measures should be used when analyzing diagnostic tools to identify patients with pathologic complete response (pCR) after NST? (ii) How can we assess response with the least morbidity and best accuracy possible? (iii) What oncological consequences may ensue if we rely on a nonsurgical-generated diagnosis of, for example, minimally invasive biopsy proven pCR, knowing that we may miss minimal residual disease in some cases? (iv) How should we design clinical trials on de-escalation of surgical treatment after NST?

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Objective: We evaluated the ability of minimally invasive, image-guided vacuum-assisted biopsy (VAB) to reliably diagnose a pathologic complete response in the breast (pCR-B). Summary Background Data: Neoadjuvant systemic treatment (NST) elicits a pathologic complete response in up to 80% of women with breast cancer. In such cases, breast surgery, the gold standard for confirming pCR-B, may be considered overtreatment. Methods: This multicenter, prospective trial enrolled 452 women presenting with initial stage 1-3 breast cancer of all biological subtypes. Fifty-four women dropped out; 398 were included in the full analysis. All participants had an imaging-confirmed partial or complete response to NST and underwent studyspecific image-guided VAB before guideline-adherent breast surgery. The primary endpoint was the false-negative rate (FNR) of VAB-confirmed pCR-B.Results: Image-guided VAB alone did not detect surgically confirmed residual tumor in 37 of 208 women [FNR, 17.8%; 95% confidence interval (CI), 12.8-23.7%]. Of these 37 women, 12 (32.4%) had residual DCIS only, 20 (54.1%) had minimal residual tumor (<5 mm), and 19 of 25 (76.0%) exhibited invasive cancer cellularity of 10%. In 19 of the 37 cases (51.4%), the false-negative result was potentially avoidable. Exploratory analysis showed that performing VAB with the largest needle by volume (7-gauge) resulted in no false-negative results and that combining imaging and imageguided VAB into a single diagnostic test lowered the FNR to 6.2% (95% CI, 3.4%-10.5%). Conclusions: Image-guided VAB missed residual disease more often than expected. Refinements in procedure and patient selection seem possible and necessary before omitting breast surgery.

PURPOSE Financial burden caused by cancer treatment is associated with material loss, distress, and poorer outcomes. Financial resources exist to support patients but identification of need is difficult. We sought to develop and test a tool to accurately predict an individual's risk of financial toxicity based on clinical, demographic, and patient-reported data prior to initiation of breast cancer treatment. PATIENTS AND METHODS We surveyed 611 patients undergoing breast cancer therapy at MD Anderson Cancer Center. We collected data using the validated COmprehensive Score for financial Toxicity (COST) patient-reported outcome measure alongside other financial indicators (credit score, income, and insurance status). We also collected clinical and perioperative data. We trained and tested an ensemble of machine learning (ML) algorithms (neural network, regularized linear model, support vector machines, and a classification tree) to predict financial toxicity. Data were randomly partitioned into training and test samples (2:1 ratio). Predictive performance was assessed using area-under-the-receiver-operating-characteristics-curve (AUROC), accuracy, sensitivity, and specificity. RESULTS In our test sample (N = 203), 48 of 203 women (23.6%) reported significant financial burden. The algorithm ensemble performed well to predict financial burden with an AUROC of 0.85, accuracy of 0.82, sensitivity of 0.85, and specificity of 0.81. Key clinical predictors of financial burden from the linear model were neoadjuvant therapy (βregularized, .11) and autologous, rather than implant-based, reconstruction (βregularized, .06). Notably, radiation and clinical tumor stage had no effect on financial burden. CONCLUSION ML models accurately predicted financial toxicity related to breast cancer treatment. These predictions may inform decision making and care planning to avoid financial distress during cancer treatment or enable targeted financial support. Further research is warranted to validate this tool and assess applicability for other types of cancer.