The clinical dilemma at the heart of the research is well known to oncologists. Patients with locally advanced gastric cancer frequently receive neoadjuvant chemotherapy before surgery to shrink tumors and improve resection outcomes. After the operation, many are offered additional adjuvant chemotherapy, yet the evidence supporting this second round of treatment in patients who have already received preoperative therapy remains contested. Some trials suggest benefit, others show marginal gains, and clinicians are left weighing toxic side effects against uncertain rewards for each individual patient. Current decisions rely heavily on post-surgical pathological staging, a blunt instrument that captures little of the biological and clinical nuance that determines whether a particular patient will relapse.

To address this gap, a multi-institutional team led by researchers at the National Cancer Center/Cancer Hospital of the Chinese Academy of Medical Sciences, together with colleagues from Tianjin Medical University Cancer Institute and Hospital, Beijing Friendship Hospital, and the Cancer Hospital of China Medical University, assembled a retrospective cohort of 1,150 patients treated with neoadjuvant chemotherapy and radical gastrectomy across four Chinese centers. Rather than relying on a single algorithm, the team embraced a large-scale combinatorial strategy. They first employed eleven different machine learning learners, each of which identified its own optimal subset of predictive features from the clinical data. These eleven feature subsets were then crossed with the eleven learners, producing 121 candidate prediction models that competed against one another for predictive supremacy.

The evaluation process was rigorous and multidimensional. The researchers assessed each candidate using the concordance index, a standard measure of how well a survival model ranks patients by risk; time-dependent receiver operating characteristic curves, which capture discrimination at specific follow-up horizons; time-dependent calibration curves, which test whether predicted probabilities match observed outcomes over time; and decision curve analysis, which quantifies the net clinical benefit of acting on the model’s predictions. Out of the 121 contenders, one model clearly rose above the rest: the GAMB-AORSF model, an acronym that combines a Generalized Additive Models via Gradient Boosting-selected feature subset with an Accelerated Oblique Random Survival Forest learner.

The technical architecture of the winning model reflects two complementary strengths. Gradient-boosted generalized additive models are highly effective at screening large sets of candidate variables and selecting a compact, informative feature subset without imposing rigid linear assumptions. The accelerated oblique random survival forest, in turn, is a tree-based ensemble method designed specifically for censored survival data. Unlike conventional random forests, which split data on single variables at each node, oblique random survival forests consider linear combinations of variables, allowing them to capture more complex interaction structures in the data. The “accelerated” designation refers to computational optimizations that make this demanding approach feasible at scale. This pairing proved exceptionally powerful for modeling time-to-recurrence outcomes.

The performance numbers are striking. In the training cohort, the GAMB-AORSF model achieved a concordance index of 0.864, and it maintained robust discrimination in two independent validation cohorts, with C-indices of 0.813 and 0.789. A C-index of 0.5 would correspond to random guessing, while 1.0 represents perfect ranking; values above 0.8 in external validation are rarely achieved in oncology prediction models, particularly those built from routinely collected clinical variables rather than expensive molecular profiling. The model also successfully stratified patients into distinct risk groups whose survival trajectories diverged substantially, providing a practical foundation for treatment personalization.

The most clinically consequential finding, however, came from the model’s use as a treatment-selection instrument. Because decisions about adjuvant chemotherapy are not randomized in routine practice, the researchers applied inverse probability of treatment weighting, a statistical technique that simulates the balance of a randomized trial by reweighting treated and untreated patients according to their probability of receiving treatment. Within the model-defined risk strata, a clear pattern emerged. High-risk patients derived significant survival benefits from adjuvant chemotherapy: across the cohorts, the treatment extended three-year restricted mean survival time by five to seven months and produced an absolute reduction in recurrence risk of 14 to 20 percent. In contrast, low-risk patients showed no significant survival improvement from additional chemotherapy, implying that many of these patients may be enduring weeks of toxic treatment with little to show for it.

Restricted mean survival time deserves particular attention as an outcome measure. Unlike hazard ratios, which can be difficult to interpret when treatment effects vary over time, restricted mean survival time quantifies the average amount of life or disease-free time gained over a fixed horizon, expressed in familiar units of months. For a high-risk patient, gaining five to seven months of cancer-free survival is a clinically meaningful benefit that most would consider worth the side effects of chemotherapy. For a low-risk patient, an unmeasurable benefit against real toxicity argues for de-escalation. The model effectively converts a population-level debate into an individual-level decision.

The study’s use of SHAP values, a game-theoretic approach to explaining machine learning predictions, further addresses a common criticism of artificial intelligence in medicine: the black-box problem. By quantifying each feature’s contribution to individual predictions, SHAP analysis allows clinicians to see why a particular patient was classified as high or low risk, fostering the transparency needed for clinical adoption. The researchers also evaluated their model against the PROBAST framework for prediction model risk of bias and reported their work following TRIPOD guidelines, signaling attention to methodological standards that many published clinical prediction tools lack.

The implications extend beyond gastric cancer. Neoadjuvant chemotherapy followed by surgery is increasingly the standard of care for multiple solid tumors, and in each setting the same question arises: after a response to preoperative therapy, does everyone still need postoperative treatment? The Chinese team’s approach, combining exhaustive model search with causal inference methods to estimate treatment effects within risk strata, offers a template that could be adapted to esophageal, rectal, and other cancers. The framework of using machine learning not merely to predict outcomes but to guide de-escalation decisions represents a shift toward genuinely precision-guided perioperative oncology.

Caveats remain. The study is retrospective, and despite external validation across multiple centers, the findings arise from a Chinese patient population treated largely with regimens such as SOX, FLOT, and DOC, which may limit immediate generalizability to other populations and treatment protocols. Prospective validation, ideally in a randomized trial design where the model is used to stratify treatment assignment, will be needed before the GAMB-AORSF model can change guidelines. Integration with emerging biomarkers such as circulating tumor DNA, which the authors note as a future direction, could further sharpen the model’s risk distinctions.

Funding for the work came from the National Natural Science Foundation of China, the Beijing Natural Science Foundation, the Capital Health Development Research Special Fund, and Tianjin medical research programs. The corresponding authors are Quan Xu, Guoliang Zheng, and Yantao Tian, with Xu Liu, Peng Jin, Peng Wang, Xinxin Shao, and Haikuo Wang contributing equally as first authors. The study received institutional review board approval and written informed consent from all participants, and the authors declared no competing interests.

For now, the message for patients and clinicians is one of cautious optimism. A tool that reliably separates those who need continued treatment from those who do not could spare hundreds of thousands of patients worldwide unnecessary chemotherapy each year, while concentrating intensive treatment on those most likely to relapse. As machine learning models like GAMB-AORSF move toward prospective testing, the era of one-size-fits-all postoperative care in gastric cancer may finally be drawing to a close.

TNBC, characterized by the absence of estrogen, progesterone, and HER2 receptors, represents roughly 15-20% of breast cancers and is notoriously associated with poor prognosis and limited therapeutic options. Neoadjuvant chemotherapy—administered before surgical intervention—has emerged as an essential approach for tumor shrinkage and increasing operability. A pathological complete response, defined as no residual invasive cancer detectable in breast and lymph nodes after treatment, traditionally correlates with favorable outcomes. However, relapse remains a sobering threat in a significant subset, demanding a refined understanding beyond pCR status alone.

The European GAMBIT consortium orchestrated one of the largest multinational prospective registries, aggregating real-world clinical and molecular data from over 1,000 TNBC patients treated with various neoadjuvant regimens. By integrating high-resolution genomic profiling with detailed clinical follow-ups, the study mapped relapse patterns across a diverse cohort, identifying distinct molecular features that delineate differential relapse risks despite apparent complete eradication of measurable disease.

One of the pivotal revelations was the heterogeneity within pCR responders. Contrary to earlier assumptions that pCR equates to uniform good prognosis, GAMBIT delineated several subgroups with significantly altered relapse timelines and sites. Through advanced bioinformatics modeling, the team discovered that specific genetic alterations—such as persistent mutations in TP53 or copy number variations in DNA damage response genes—serve as biomarkers predicting early versus late relapse, challenging the one-size-fits-all paradigm of post-pCR risk management.

Furthermore, spatial relapse patterns illuminated by the GAMBIT study underscored the proclivity of TNBC to metastasize aggressively to visceral organs, including lungs and liver, even after achieving pCR. This compels reconsideration of surveillance imaging schedules and therapeutic intensification in patients flagged as high risk by the newly validated molecular classifiers. The integration of circulating tumor DNA monitoring also emerged as a promising adjunct, capable of noninvasively detecting minimal residual disease and heralding relapse onset months before clinical manifestations.

Technological advancements in single-cell sequencing and multiplex immunohistochemistry empowered the researchers to decode the tumor microenvironment’s role in relapse propensity. Specifically, immune infiltration profiles revealed that the presence of exhausted CD8+ T-cell phenotypes coupled with suppressive myeloid populations correlated with diminished long-term remission, despite histologic clearance of tumor cells. This insight paves the way for incorporating immunomodulatory therapies in the adjuvant setting to bolster anti-tumor immunity among vulnerable pCR patients.

The European GAMBIT study also addressed the influence of tumor heterogeneity and clonal evolution under therapeutic pressure. Deep sequencing analyses demonstrated that subclonal populations harboring resistant genotypes could evade systemic chemotherapy, silently persisting and manifesting as relapse. This evolutionary perspective advocates for combination regimens targeting multiple vulnerabilities within the tumor architecture, potentially deploying synchronized immunotherapy or targeted agents alongside traditional chemotherapy.

Clinically, the implications of these findings are profound. Current guidelines predominantly use clinical and pathologic factors to guide adjuvant therapy decisions following neoadjuvant treatment. By incorporating molecular risk stratification, oncologists can identify subsets of high-risk patients who might benefit from intensified surveillance, novel maintenance therapies, or enrollment in clinical trials exploring cutting-edge treatments. Conversely, low-risk patients could be spared the morbidity associated with overtreatment, embodying the principles of precision medicine.

In addition, the real-world nature of the study lends strong external validity to its conclusions. Unlike tightly controlled clinical trials that often exclude patients with comorbidities or diverse demographic backgrounds, GAMBIT’s inclusive cohort mirrors routine clinical practice, enhancing the applicability of its prognostic models across European populations. This openness bodes well for the generalizability and potential adoption of molecular diagnostic tools derived from the study.

Key to the study’s success was the collaborative framework that harmonized data collection and analysis across multiple European cancer centers, setting a precedent for future multinational consortia targeting hard-to-treat malignancies. The integration of genomic sequencing, bioinformatics, and clinical data underscores the imperative of interdisciplinary synergy to unravel the complexities of cancer biology and translate discoveries into actionable clinical strategies.

Looking forward, the GAMBIT consortium is poised to expand its research scope by incorporating immunophenotyping, metabolomics, and longitudinal liquid biopsy data, endeavoring to construct dynamic models of tumor evolution and relapse prediction. Such advancements could revolutionize follow-up protocols and rapidly identify patients at incipient risk, enabling preemptive interventions.

Moreover, the study’s findings stimulate exploration of targeted therapeutics aimed at the molecular aberrations implicated in residual disease. Agents modulating DNA repair pathways, inhibitors of checkpoint kinases, or metabolic modulators tailored to the tumor’s unique genomic landscape hold promise as adjuncts in the adjuvant setting. Future clinical trials will be essential to validate these approaches and translate molecular insights into survival benefits.

In conclusion, the European GAMBIT study marks a paradigm shift in the post-neoadjuvant management of triple-negative breast cancer by demonstrating that pathological complete response alone is insufficient to fully stratify relapse risk. The comprehensive molecular characterization and real-world clinical correlations offer a new lens through which to view remission and recurrence, enabling precision oncology to fulfill its promise in one of the most challenging breast cancer subtypes. This research not only enriches our understanding of tumor biology but also charts a course for personalized surveillance and therapeutic intervention, providing hope for improved outcomes in TNBC patients worldwide.

Subject of Research: Risk stratification and relapse patterns in triple-negative breast cancer patients achieving pathological complete response after neoadjuvant therapy.

Article Title: Risk stratification and relapse pattern in triple-negative breast cancer with pathological complete response after neoadjuvant treatment: the European GAMBIT real-world study.

Article References:
Massa, D., Foukakis, T., Giacchetti, S. et al. Risk stratification and relapse pattern in triple-negative breast cancer with pathological complete response after neoadjuvant treatment: the European GAMBIT real-world study. Nat Commun (2026). https://doi.org/10.1038/s41467-026-74056-2

Image Credits: AI Generated

Gastric cancer, a malignancy noted for its aggressive nature and complex anatomical considerations, often necessitates radical surgical interventions post neoadjuvant chemotherapy. Neoadjuvant therapy, which aims to reduce tumor burden before surgery, can influence tissue characteristics and subsequent surgical maneuverability. Hence, understanding how different surgical techniques fare under these nuanced conditions is critical. The researchers meticulously stratified patients into three cohorts based on the surgical technique: totally laparoscopic total gastrectomy, laparoscopic-assisted total gastrectomy, and open total gastrectomy, enabling a robust comparative analysis.

One of the noteworthy findings was the paradoxical observation that the totally laparoscopic group exhibited a longer operative time compared to the other two groups. This could initially be perceived as a disadvantage given surgical time often correlates with complications and recovery. However, the increased duration may reflect the inherent complexity and precision required during minimally invasive procedures, which demand advanced technical expertise and cautious dissection in the altered anatomical field post-chemotherapy. This nuanced detail underscores the evolving surgical learning curve associated with robotic and laparoscopic techniques.

Contrastingly, the study also revealed that the totally laparoscopic group experienced greater intraoperative blood loss compared to laparoscopic-assisted and open surgery patients. This finding invites intriguing questions regarding vascular control and tissue management during purely laparoscopic resections. Despite this, the rate of intraoperative blood transfusion did not significantly differ between groups, suggesting that hemodynamic stability was maintained across all surgical approaches, potentially due to effective anesthesiological protocols and surgical expertise.

A decisive advantage of the totally laparoscopic approach emerged in postoperative recovery metrics. Patients undergoing totally laparoscopic total gastrectomy demonstrated accelerated gastrointestinal function recovery—manifested in earlier postoperative bowel movements, defecations, and resumption of oral intake. These parameters are pivotal as they correlate strongly with reduced hospitalization durations, minimized catheter and drain-associated morbidities, and improved overall patient comfort and outcomes. Such benefits hold substantial promise in enhancing patient quality of life and reducing healthcare resource burdens.

Further examination of oncological efficacy revealed that the total number of lymph nodes dissected was significantly higher in the totally laparoscopic group. Lymphadenectomy extent is a critical determinant of accurate pathological staging and prognostication in gastric cancer. The ability of laparoscopy to facilitate meticulous lymph node harvest despite spatial limitations challenges the traditional notion that open surgeries offer superior oncological clearance. This is a vital affirmation for minimally invasive oncologic surgery proponents advocating for laparoscopic modalities.

Interestingly, critical parameters such as tumor staging, maximum tumor diameter, and number of positive lymph nodes harvested were statistically equivalent across all groups, implying comparable surgical radicality and cancer control. Similarly, postoperative complications measured by Clavien-Dindo classifications and readmission rates at both 30 and 90 days post-surgery did not diverge substantially among the surgical strategies, reinforcing the safety profile of minimally invasive techniques in carefully selected cases.

Hospitalization time, a significant economic and emotional factor for patients, also did not differ significantly among the groups. This outcome suggests that while total laparoscopic gastrectomy offers faster functional recovery, other variables—such as institutional practices, patient comorbidities, and rehabilitation protocols—may homogenize length of stay. It highlights the multifaceted nature of postoperative care beyond surgical technique alone.

Survival outcomes, arguably the most critical endpoint, were comparable across the board. There were no significant differences in overall or disease-free survival between the groups, indicating that the choice of surgical method does not compromise long-term oncologic prognosis in advanced gastric cancer patients treated with neoadjuvant therapy. This revelation may grant surgeons and patients greater flexibility in choosing less invasive methods without the fear of diminishing survival chances.

This study’s multicenter design and relatively large sample size bolster its external validity, offering a more generalized reflection of clinical realities across varied clinical settings. Additionally, the retrospective nature allows for real-world outcome analysis, although prospective randomized studies would be invaluable to corroborate these findings and further refine surgical guidelines.

It is important to contextualize these findings within the broader surgical oncology field, where minimally invasive techniques have steadily revolutionized management paradigms. This research illuminates that despite initial challenges such as longer operative times and technical demands, total laparoscopic gastrectomy champions faster postoperative recuperation without sacrificing oncological rigor.

The implications of earlier postoperative recovery extend beyond clinical indices; they resonate deeply with patient-centric care models emphasizing reduced morbidity, quicker return to daily activities, and psychological well-being. Early food intake and bowel function restoration can significantly lessen patient anxiety and improve nutritional statuses, crucial for prolonged recovery and adjuvant treatment tolerance.

Moreover, the higher lymph node yield observed with the fully laparoscopic approach may partially be attributed to enhanced visualization and magnification offered by laparoscopic optics, enabling surgeons to conduct more precise dissections. This technical superiority could redefine surgical expectations in complex gastrectomy cases previously thought to mandate open surgery.

While the surgical landscape continues to embrace robotic assistance and novel instrumentation, this study’s insights underscore the sufficiency of laparoscopy alone as a formidable tool, particularly in resource-limited centers where robotic platforms remain elusive. The study advocates for the inclusion of total laparoscopic techniques in standard surgical armamentaria for advanced gastric cancer when appropriate expertise is available.

It must also be acknowledged that patient selection criteria and surgical expertise inherently influence outcomes. As such, the absence of significant differences in survival and complication rates likely reflects optimal patient stratification and experienced surgical teams across participating centers. These factors highlight the centrality of multidisciplinary collaboration in oncologic care.

To summarize, this pioneering investigation confirms that while total laparoscopic total gastrectomy requires lengthier operative times, it offers incontrovertible benefits in enhanced postoperative recovery without compromising oncological safety or long-term survival. These findings may encourage more widespread adoption of minimally invasive approaches as standard practice following neoadjuvant chemotherapy in advanced gastric cancer care.

As gastric cancer treatment continues to evolve towards precision and personalized strategies, surgical innovation remains paramount. Studies such as this one not only validate emerging techniques but also provide a roadmap for balancing surgical efficiency, patient recovery, and oncologic integrity, fulfilling the overarching goal of improving outcomes in this formidable disease.

Subject of Research: Evaluation of short- and long-term outcomes among different surgical approaches (total laparoscopic, laparoscopic-assisted, and open total gastrectomies) for advanced gastric cancer patients after neoadjuvant chemotherapy.

Article Title: Comparison of short- and long-term outcomes among total laparoscopic, laparoscopic-assisted, and open total gastrectomies for advanced gastric cancer patients after neoadjuvant chemotherapy: a multicenter retrospective cohort study.

Article References:
Gao, X., Li, Z., Yuan, J. et al. Comparison of short- and long-term outcomes among total laparoscopic, laparoscopic-assisted, and open total gastrectomies for advanced gastric cancer patients after neoadjuvant chemotherapy: a multicenter retrospective cohort study. BMC Cancer 25, 1401 (2025). https://doi.org/10.1186/s12885-025-14765-x

Image Credits: Scienmag.com

DOI: https://doi.org/10.1186/s12885-025-14765-x

One of the defining clinical challenges in managing TNBC lies in addressing residual disease post-neoadjuvant treatment. Patients who fail to achieve a pathological complete response tend to have significantly higher relapse rates and diminished survival prospects. The MIRINAE trial responds directly to this unmet need by examining whether incorporating atezolizumab, which blocks the PD-L1 immune checkpoint, can enhance the efficacy of the standard capecitabine monotherapy. This immunotherapy-chemotherapy duo aims to galvanize the patient’s immune system to better recognize and destroy the remaining cancer cells, potentially altering the future landscape of TNBC adjuvant treatment.

The trial is meticulously designed with invasive disease-free survival (IDFS) at five years as its primary endpoint, a critical measure that reflects the duration patients remain free from recurrence or new tumor development. The inclusion of IDFS as a central evaluation metric exemplifies the study’s commitment to gauging meaningful clinical benefit rather than solely short-term responses. Furthermore, secondary endpoints broaden the scope of assessment by analyzing IDFS in PD-L1 positive subsets, distant relapse-free survival (DRFS), and overall survival (OS), offering a comprehensive survival analysis that encompasses both localized and systemic disease control.

Atezolizumab’s mechanism targets the PD-L1 protein expressed on tumor cells or infiltrating immune cells, which, when engaged, effectively suppresses the immune response against the tumor by inactivating T-cells. By interrupting this pathway, atezolizumab reactivates cytotoxic T-cell function, thereby enhancing anti-tumor immune surveillance. When combined with capecitabine — a chemotherapy agent that causes DNA damage selectively in proliferating cancer cells — the regimen ideally synergizes cytotoxic effects with immune modulation. The trial seeks to clarify if this synergy translates into prolonged disease remission and survival.

Recognizing the limitations of previous landmark trials such as KEYNOTE-522, which primarily focused on neoadjuvant immunotherapy, the MIRINAE trial strategically fills a critical evidence gap by focusing on adjuvant therapy following surgery. While KEYNOTE-522 demonstrated the benefits of adding pembrolizumab to chemotherapy before surgery for early-stage TNBC, uncertainties remain for patients with residual disease. MIRINAE’s exploration of atezolizumab post-neoadjuvant completion targets this high-risk subgroup, potentially setting new therapeutic standards in the adjuvant setting.

From a safety standpoint, the phase II trial rigorously monitors adverse events to ensure that the combination therapy’s toxicity profile remains acceptable. Immunotherapy, although promising, carries risks of immune-related adverse effects including inflammation of diverse organs, necessitating careful vigilance. Additionally, capecitabine’s established side effect profile involving hand-foot syndrome, diarrhea, and hematologic toxicities must be balanced against therapeutic gain. The trial’s safety data will be paramount in validating whether the combined regimen can be safely incorporated into routine clinical practice.

The MIRINAE trial enrolled patients diagnosed with triple-negative breast cancer who completed standard neoadjuvant chemotherapy and demonstrated residual invasive disease. This patient population, notorious for poor prognosis and high recurrence, offers a crucial test bed for novel interventions. By focusing on residual cancer after initial treatment, the trial strategically targets those most in need of effective adjuvant therapies—possibly redefining management algorithms for this vulnerable cohort.

Regarding biomarker analysis, PD-L1 expression serves as a pivotal stratification factor, given its role in modulating response to immune checkpoint inhibitors. The trial’s secondary endpoint addressing IDFS in PD-L1 positive patients will potentially illuminate predictive markers for responsiveness, guiding personalized treatment approaches. Such precision oncology initiatives underscore the movement towards tailoring cancer therapies based on tumor immunobiology, maximizing efficacy while minimizing unnecessary toxicity.

The significance of the MIRINAE trial extends beyond its immediate clinical intent. It integrates the latest immuno-oncology advances with chemotherapy paradigms, striving for durable remission in a disease historically marked by early relapses and limited targeted options. As the oncology community awaits mature data from this trial, the potential to pivot triple-negative breast cancer treatment into a new era of immunotherapy-enhanced adjuvant regimens is palpable.

Moreover, the clinical trial’s registration at ClinicalTrials.gov (NCT03756298) represents global transparency and allows the international research community to track ongoing progress and outcomes, fostering collaboration and knowledge dissemination. The publication in BMC Cancer further guarantees wide accessibility to the trial’s methodology and preliminary results, enabling scientific discourse and subsequent validation studies.

By addressing the post-neoadjuvant therapeutic gap, the MIRINAE trial provides the foundation for evidence-based refinements in managing high-risk TNBC patients. Should the combination of atezolizumab and capecitabine validate superior efficacy with manageable safety, it could shift current standard-of-care paradigms, offering patients a vital lifeline in a disease notorious for its aggressive clinical course.

The MIRINAE trial’s investigative design offers an exemplar of modern clinical research: thoughtful consideration of tumor biology, integration of cutting-edge immunotherapy, and robust clinical endpoints that matter most to patients. It stands as a testament to multidisciplinary cancer research efforts converging to combat the formidable challenge of triple-negative breast cancer.

As the oncology field embraces precision medicine, trials like MIRINAE emphasize the critical intersection between immunology and oncology, exploring novel therapeutics that specifically target tumor microenvironment dynamics. This approach heralds a hopeful future where cancer care is increasingly personalized, effective, and curative for even the most aggressive malignancies.

In conclusion, the MIRINAE phase II trial stands at the forefront of translational cancer research, potentially unveiling a new paradigm for adjuvant therapy in TNBC patients with residual disease post-neoadjuvant chemotherapy. Its outcomes could influence clinical guidelines worldwide, signify a meaningful advance in therapeutic strategies, and ultimately improve survival and quality of life for this challenging patient population.

Subject of Research: Evaluation of adjuvant atezolizumab plus capecitabine versus capecitabine monotherapy in triple-negative breast cancer patients with residual invasive cancer after neoadjuvant chemotherapy.

Article Title: Randomized, phase II trial to evaluate the efficacy and safety of atezolizumab plus capecitabine adjuvant therapy compared to capecitabine monotherapy for triple receptor-negative breast cancer with residual invasive cancer after neoadjuvant chemotherapy (MIRINAE trial, KCSG-BR18-21).

Article References:
Lee, J., Ahn, H.K., Lee, KH. et al. Randomized, phase II trial to evaluate the efficacy and safety of atezolizumab plus capecitabine adjuvant therapy compared to capecitabine monotherapy for triple receptor-negative breast cancer with residual invasive cancer after neoadjuvant chemotherapy (MIRINAE trial, KCSG-BR18-21). BMC Cancer 25, 1295 (2025). https://doi.org/10.1186/s12885-025-14673-0

Image Credits: Scienmag.com

DOI: https://doi.org/10.1186/s12885-025-14673-0