Breast Cancer Research and Treatment最新文献

Purpose: Many breast centers are unable to provide immediate results at the time of screening mammography which results in delayed patient care. Implementing artificial intelligence (AI) could identify patients who may have breast cancer and accelerate the time to diagnostic imaging and biopsy diagnosis.

Methods: In this prospective randomized, unblinded, controlled implementation study we enrolled 1000 screening participants between March 2021 and May 2022. The experimental group used an AI system to prioritize a subset of cases for same-visit radiologist evaluation, and same-visit diagnostic workup if necessary. The control group followed the standard of care. The primary operational endpoints were time to additional imaging (T_A) and time to biopsy diagnosis (T_B).

Results: The final cohort included 463 experimental and 392 control participants. The one-sided Mann-Whitney U test was employed for analysis of T_A and T_B. In the control group, the T_A was 25.6 days [95% CI 22.0-29.9] and T_B was 55.9 days [95% CI 45.5-69.6]. In comparison, the experimental group's mean T_A was reduced by 25% (6.4 fewer days [one-sided 95% CI > 0.3], p<0.001) and mean T_B was reduced by 30% (16.8 fewer days; 95% CI > 5.1], p=0.003). The time reduction was more pronounced for AI-prioritized participants in the experimental group. All participants eventually diagnosed with breast cancer were prioritized by the AI.

Conclusions: Implementing AI prioritization can accelerate care timelines for patients requiring additional workup, while maintaining the efficiency of delayed interpretation for most participants. Reducing diagnostic delays could contribute to improved patient adherence, decreased anxiety and addressing disparities in access to timely care.

Background: The heterogeneity of breast cancer (BC) necessitates the identification of novel subtypes and prognostic models to enhance patient stratification and treatment strategies. This study aims to identify novel BC subtypes based on PANoptosis-related genes (PRGs) and construct a robust prognostic model to guide individualized treatment strategies.

Methods: The transcriptome data along with clinical data of BC patients were sourced from the TCGA and GEO databases. Consensus clustering was performed on 12 PRGs to ascertain potential BC subtypes, and variances in survival, infiltration of immune cells, and functional pathways among them were examined. A prognostic model was generated through 101 combinations of machine learning algorithms and validated across multiple cohorts. The response of patients towards immunotherapy were analyzed using multiple frameworks.

Results: Consensus clustering of 12 PRGs identified two distinct BC subtypes, with subtype B exhibiting significantly lower overall survival (OS) rates compared to subtype A. Immune cell infiltration analysis revealed higher immune activity in subtype A. Functional pathway analysis revealed that subtype A exhibited a significant enrichment in immune-related pathways, while subtype B was associated with cell cycle and metabolic processes. An integrated machine learning framework integrating CoxBoost and Random Survival Forest (RSF) algorithms was developed, demonstrating high predictive performance across multiple cohorts. A nomogram combining age and risk score was constructed, showing excellent predictive performance. Immune landscape analysis revealed that the high-risk group exhibited a suppressive tumor immune microenvironment (TIME). Immunotherapy response prediction suggested that low-risk patients were more likely to benefit from PD-1 and CTLA-4 inhibitors.

Conclusions: Our study provides a comprehensive framework for BC subtype classification and prognostic prediction, offering valuable insights for personalized treatment strategies.

Purpose: Individuals with metastatic breast cancer (MBC) may live with their disease for many years. We initiated the Johns Hopkins Hope at Hopkins Clinic to assess the needs and optimize the care of these patients.

Patients and methods: Patients with MBC who agreed to participate in the Clinic in addition to usual care completed patient-reported outcome (PRO) surveys. They met with a navigator and underwent core consults (cancer rehabilitation, integrative medicine, supportive and palliative care, social work, and nutrition), clinical trial eligibility assessment, and optional services based on PRO responses and selection from a Clinic Menu. A medical oncologist provided a Care Plan during a final consult. Participants were asked to complete 3- and 6-month follow-up PRO surveys. We report on initial Clinic implementation, participant characteristics, and baseline PROs.

Results: From 11/2020 to 6/2022, 45 patients completed baseline surveys and participated in the Clinic. Median age was 58 (32-86); the majority (71%) were white and had estrogen receptor-positive (84%) tumors. Baseline physical and mental health were not good for ≥ 14 days of the past month for 22 and 10%, respectively. PROMIS measure scores were > 1 standard deviation worse than average for 32% for Physical Health, 16% for Mental Health, and 23% for Physical Function. PHQ-8 and GAD-7 scores suggested depression and anxiety for 22 and 7%, respectively. More than 80% of participants received specific recommendations from the core consultants. Only 20% of participants completed follow-up surveys.

Conclusion: Patients living with MBC have multiple needs. We used our results to implement routine PRO assessments and to expand services for patients with MBC. Our experience can serve as a model for coordinated care in other systems.

Purpose: We examined the impact of the COVID-19 consortium recommendations on the surgical management of breast cancer during the first year of the pandemic.

Methods: Patients with newly diagnosed ER + DCIS, ER- DCIS, AJCC Stage cT1-2N0-1 ER + , HER2-, HER2 + , and triple negative breast cancer were identified from the National Cancer Database from 2018 to 2021. An interrupted time series design evaluated differences in surgical delay and use of neoadjuvant chemotherapy/immunotherapy (NAC) and endocrine therapy (NET) before and after the pandemic.

Results: A total of 895116 female patients were included in the study with a mean age of 61.7 years. Time to surgery decreased by an average 5.5 days from January 2020 to May 2020 for all breast cancer types, corresponding with a 62.2% decrease in breast cancer diagnoses per month from January 2020 to April 2020. The use of NET increased from 5.6 to 23.6% from January to March 2020 for patients with ER + DCIS and 8.0 to 31.1% for ER + cT1-2N0 cancer (both p < 0.01). The use of NAC for HER2 + tumors and triple negative breast cancers has been increasing since 2018 and a larger than expected increase was seen from 57.2 to 63.6% for HER2 + tumors and 55.6 to 68.7% for triple negative breast cancers (both p < 0.01). Treatment practices returned to pre-pandemic levels in June 2020.

Conclusion: Prior to the publication of the Consortium recommendations, time to surgery decreased while the use of NET and NAC increased, with the resumption of pre-pandemic practices by June 2020.

Background: In New Zealand, BreastScreen Aotearoa (BSA), a biennial national breast screening programme, was implemented in 1998. This study examines the incidence trends of ductal carcinoma in situ (DCIS) in New Zealand women from 1999 to 2022.

Methods: All women with a primary diagnosis of DCIS over the 24-year study period were identified from the New Zealand Cancer Registry and BSA records. Age-standardised incidence rates (ASIR), detection rates (ASDR) and average annual percent changes were calculated.

Results: The annual ASIR was 13.5 per 100,000 New Zealand women, and increased by 0.91% (95% confidence interval (CI): 0.26%, 1.66%) annually. Among women aged 45-69 years during 2006-2022, the annual ASIR was 36.3 for programme-detected DCIS, increasing 1.29% (95%CI: 0.13%, 2.73%) per year, and 14.2 for non-programme-detected DCIS, with no significant changes over the study period. The programme-detected ASIRs were highest for Pacific (38.6), Asian (38.2), and Māori (38.0) women. The programme ASDR was 0.55 per 1000 women screened, with no significant changes over time, and was highest for Asian (0.69), and Māori and Pacific (both at 0.65) women.

Conclusion: DCIS incidence increased in New Zealand women from 1999 to 2022, driven by an increase in screening participation, and varied by ethnicity.

Purpose: Understanding real-world treatment patterns and their effectiveness in HR + HER2- advanced breast cancer (aBC) in Canadian patients.

Patient and methods: This was a multi-center, observational, prospective cohort study including men and pre-/peri-/postmenopausal women with HR + HER2- aBC receiving endocrine therapy (ET) or ET + targeted therapy (ET + TT). The primary objective was duration of treatment (DOT) with ET and ET + TT. Sequence of therapies, treatment patterns, and Overall Survival (OS) were also evaluated.

Results: DOT was prolonged in patients receiving ET + TT compared to ET (median DOT: ET + TT 397 days vs ET 192 days; Log-Rank test p value < .0001; HR = 0.66; 95% CI; 0.52, 0.85). An extended DOT was observed in ET + CDK4/6i subgroup when compared to ET (median DOT: ET + CDK4/6i 601 days vs ET 192 days; Log-Rank test p value < .0001). This increase was statistically significant irrespective of line of therapy at baseline (1L: median DOT: ET + CDK4/6i: 649 days vs ET: 217 days, p value =  < .0001; 2L: median DOT: ET + CDK4/6i: 487 days vs ET: 203 days, p value = 0.0013; 3L: median DOT: ET + CDK4/6i: 597 days vs ET: 143 days therapy: p value = 0.0006). ET alone and ET + CDK4/6i were the most frequently administered therapies in both 1st (ET alone: 43.5% and ET + CDK4/6i: 43.3%) and 2nd lines (ET alone: 36.3% and ET + CDK4/6i: 24.6%). Among patients who received at least one CDK4/6i in 1st, 2nd, or 3rd line, CDK4/6i were mostly administered in 1st line (61.9%) and 2nd line (38.5%).

Clinicaltrials: gov ID: NCT02753686; Registration Date:20-04-2016.

Conclusion: Results support current treatment recommendations of early introduction of CDK4/6i in HR + /HER2- aBC.

Purpose: Interstitial lung disease (ILD) is a well described and potentially fatal complication of trastuzumab-deruxtecan (T-DXd). It is currently unknown if specific monitoring is beneficial in the early detection of ILD in these patients. We describe the efficacy and feasibility of a novel ILD monitoring protocol in breast cancer patients treated with T-DXd at our institution.

Methods: An ILD monitoring protocol developed at our institution included baseline and ongoing monitoring with pulmonary function testing (PFTs) and high-resolution chest computed tomography (HRCT) at pre-specified intervals. Patients with metastatic HER2+ or HER2-low breast cancer treated at Huntsman Cancer Institute who received ≥ 1 cycle of T-DXd between 2020 and 2023 were included (n = 68). Patient outcomes and provider adherence to the protocol were retrospectively evaluated. Providers were classified as "no adherence" if they did not elect to participate in any elements of the recommended protocol or as "some adherence" if they had at least some monitoring per protocol.

Results: 10 cases of ILD were identified with an incidence of 12% (3/25) in the no adherence group and 16% (7/43) in the some adherence group. ILD cases in the no adherence group included one grade 2 and two grade 5 cases. The some adherence group included three grade 1 and four grade 2 cases.

Conclusion: An ILD monitoring protocol consisting of baseline PFTs and ongoing monitoring with PFTs and HRCT is a feasible approach as evidenced by a majority provider adherence rate. This type of protocol may be effective in preventing severe cases of ILD and identifying grade 1 events that may permit treatment re-challenge.

Purpose: Identification of the molecular subtypes in breast cancer allows to optimize treatment strategies, but usually requires invasive needle biopsy. Recently, non-invasive imaging has emerged as promising means to classify them. Magnetic resonance imaging is often used for this purpose because it is three-dimensional and highly informative. Instead, only a few reports have documented the use of mammograms. Given that mammography is the first choice for breast cancer screening, using it to classify molecular subtypes would allow for early intervention on a much wider scale. Here, we aimed to evaluate the effectiveness of combining global and local mammographic features by using Vision Transformer (ViT) and Convolutional Neural Network (CNN) to classify molecular subtypes in breast cancer.

Methods: The feature values for binary classification were calculated using the ViT and EfficientnetV2 feature extractors, followed by dimensional compression via principal component analysis. LightGBM was used to perform binary classification of each molecular subtype: triple-negative, HER2-enriched, luminal A, and luminal B.

Results: The combination of ViT and CNN achieved higher accuracy than ViT or CNN alone. The sensitivity for triple-negative subtypes was very high (0.900, with F-value = 0.818); whereas F-value and sensitivity were 0.720 and 0.750 for HER2-enriched, 0.765 and 0.867 for luminal A, and 0.614 and 0.711 for luminal B subtypes, respectively.

Conclusion: Features obtained from mammograms by combining ViT and CNN allow the classification of molecular subtypes with high accuracy. This approach could streamline early treatment workflows and triage, especially for poor prognosis subtypes such as triple-negative breast cancer.

Background: There is a paucity of data supporting the role of neutrophil-lymphocyte ratios (NLR) to determine clinicopathological parameters in patients being treated for primary breast cancer.

Aims: To evaluate the association between pre-operative NLR and clinicopathological parameters in patients diagnosed with breast cancer.

Methods: A retrospective cohort study was performed. This included consecutive patients indicated to undergo surgery for primary breast cancer at University Hospital Limerick between January 2010 and June 2017. NLR was expressed as a continuous variable. Univariable and multivariable linear regression analyses were used to determine the correlation between NLR and clinicopathological data. Data analytics was performed using SPSS v29.0.

Results: 673 patients met the inclusion criteria. Overall, the median preoperative NLR is 2.63 (standard deviation: 1.42). At univariable analysis, patient age (beta coefficient: 0.009, 95% confidence interval (CI) 0.001-0.017, P = 0.027), tumour size (beta coefficient: 0.013, 95% CI 0.005-0.021, P = 0.001), and human epidermal growth factor receptor-2 status (beta coefficient: - 0.370, 95% CI - 0.676-0.065, P = 0.017) were all predicted using NLR. However, at multivariable analysis, tumour size was the sole parameter predictable by NLR (beta coefficient: 0.011, 95% CI 0.002-0.019, P = 0.013).

Conclusions: This study demonstrates that pre-operative NLR may serve as an independent predictor of tumour size in patients being treated with primary breast cancer. Ratification of these preliminary findings is warranted before robustly adopted into clinical practice.