Grade 4 Neutropenia Frequency as a Binary Risk Predictor for Adverse Clinical Consequences of Chemotherapy­Induced Neutropenia: A Meta-analysis.

Neutropenia is the major toxicity of myelosuppressive cancer chemotherapy. Grade 4 neutropenia (Gr4N) is a measure of chemotherapy-induced neutropenia (CIN) severity. We conducted a meta-analysis of CIN data. Gr4N incidence was significantly correlated with febrile neutropenia (FN), days of severe neutropenia (DSN), and nadir absolute neutrophil count (ANC), which are all important predictors of morbidity. With a Gr4N threshold of 65%, both FN and DSN were below levels for low risk of adverse CIN outcomes. Gr4N was highly predictive for adverse CIN outcomes, and a 65% threshold demarcated low vs. high risk for FN and other adverse CIN outcomes.

Real-World Impact of Prophylactic Growth Factor Use on Timing of Febrile Neutropenia and Infection After High-Risk Chemotherapy.

BACKGROUND: Prophylactic growth-factor therapy with granulocyte colony-stimulating factor (G-CSF) reduces the risk of febrile neutropenia (FN) in patients with breast cancer initiating myelosuppressive chemotherapy. However, little is known about the protective benefit early in the chemotherapy cycle. METHODS: To assess the relationship between G-CSF prophylaxis and incidence of FN/infection in week 1 versus beyond week 1 of the first chemotherapy cycle, a retrospective study was conducted using Medicare claims from 2005 through 2020 among patients with breast cancer initiating high-risk chemotherapy. Two cohorts were compared based on G-CSF prophylaxis within 3 days following chemotherapy initiation. The primary outcome was FN or infection, defined as hospitalization with neutropenia, fever, or infection diagnosis. Secondary outcomes were a stricter definition of FN and infection-related hospitalization. Unadjusted and regression-adjusted proportions of patients experiencing each outcome during week 1 versus beyond week 1 of the first chemotherapy cycle were compared. RESULTS: Of 78,810 patients meeting all inclusion criteria (>98% female; mean age, 69 years), 79% initiated TC (docetaxel/cyclophosphamide), 14% TCH (docetaxel/carboplatin/trastuzumab), and 7% TAC (docetaxel/doxorubicin/cyclophosphamide). Among patients receiving G-CSF (74%), incidence of first-cycle FN/infection was lower compared with patients not receiving G-CSF (overall, 6% vs 13%; TAC, 12% vs 19%; TC, 6% vs 12%; TCH, 5% vs 15%). However, patients who received G-CSF were generally more likely to experience FN/infection in week 1 (adjusted odds ratio [aOR], 1.24 for all; 1.73 for TAC; 1.35 for TC; and 0.76 for TCH). Results were similar for strictly defined FN (overall aOR, 1.29 for week 1 and 0.12 for beyond week 1) and infection-related hospitalization (overall aOR, 1.33 for week 1 and 0.27 for beyond week 1). CONCLUSIONS: Overall, the rates of chemotherapy-related FN and infection in week 1 of the first chemotherapy cycle are similar for patients receiving and not receiving G-CSF, suggesting continued risk in week 1 despite prophylactic G-CSF.

Headache outcomes of a sleep behavioral intervention in breast cancer survivors: Secondary analysis of a randomized clinical trial.

BACKGROUND: Breast cancer survivors often have persisting headache. In a secondary analysis of the Brief Behavioral Therapy for Cancer-Related Insomnia (BBT-CI) clinical trial (ClinicalTrials.gov identifier NCT02165839), the authors examined the effects of BBT-CI on headache outcomes in patients with breast cancer. METHODS: Patients with breast cancer who were receiving chemotherapy were randomly assigned to receive either the BBT-CI intervention or the Healthy EAting Education Learning for healthy sleep (HEAL) control intervention, and both were delivered over 6 weeks by trained staff. Headache outcomes and heart rate variability (HRV) were measured at baseline, 6 weeks, 6 months, and 12 months. Mixed-effects models were used to examine longitudinal headache outcomes in the groups according to the intention to treat. Principal component analysis and agglomerative hierarchical clustering were conducted to reduce 16 variables for data-driven phenotyping. RESULTS: Patients in the BBT-CI arm (n = 73) exhibited a significant reduction in headache burden over time (P = .02; effect size [Cohen d] = 0.43), whereas the reduction was not significant among those in the HEAL arm (n = 66). The first principal component was positively loaded by headache, sleep, fatigue, and nausea/vomiting and was negatively loaded by cognitive, physical, and emotional functioning. Agglomerative hierarchical clustering revealed 3 natural clusters. Cluster I (n = 58) featured the highest burden of headache, insomnia, and nausea/vomiting; cluster II (n = 50) featured the lowest HRV despite a low burden of headache and insomnia; and cluster III (n = 31) showed an inverse relation between HRV and headache-insomnia, signifying autonomic dysfunction. CONCLUSIONS: BBT-CI is efficacious in reducing headache burden in breast cancer survivors. Patient phenotyping demonstrates a headache type featuring sleep disturbance, nausea/vomiting, and low physical functioning-revealing similarities to migraine. LAY SUMMARY: Breast cancer survivors often have persisting headache symptoms. In patients with cancer, treatment of chronic headache disorders using daily medications may be challenging because of drug interactions with chemotherapy and other cancer therapies as well as patients' reluctance to add more drugs to their medicine list. Headache and sleep disorders are closely related to each other. This study demonstrates that a sleep behavioral therapy reduced headache burden in breast cancer survivors. In addition, the majority of headache sufferers had a headache type with similarities to migraine-featuring sleep disturbance, nausea/vomiting, and low physical functioning.

Predicting Patient Reported Outcomes of Cognitive Function Using Connectome-Based Predictive Modeling in Breast Cancer.

Being able to predict who will likely experience cancer related cognitive impairment (CRCI) could enhance patient care and potentially reduce economic and human costs associated with this adverse event. We aimed to determine if post-treatment patient reported CRCI could also be predicted from baseline resting state fMRI in patients with breast cancer. 76 newly diagnosed patients (n = 42 planned for chemotherapy; n = 34 not planned for chemotherapy) and 50 healthy female controls were assessed at 3 times points [T1 (prior to treatment); T2 (1 month post chemotherapy); T3 (1 year after T2)], and at yoked intervals for controls. Data collection included self-reported executive dysfunction, memory function, and psychological distress and resting state fMRI data converted to connectome matrices for each participant. Statistical analyses included linear mixed modeling, independent t tests, and connectome-based predictive modeling (CPM). Executive dysfunction increased over time in the chemotherapy group and was stable in the other two groups (p < 0.001). Memory function decreased over time in both patient groups compared to controls (p < 0.001). CPM models successfully predicted executive dysfunction and memory function scores (r > 0.31, p < 0.002). Support vector regression with a radial basis function (SVR RBF) showed the highest performance for executive dysfunction and memory function (r = 0.68; r = 0.44, p's < 0.001). Baseline neuroimaging may be useful for predicting patient reported cognitive outcomes which could assist in identifying patients in need of surveillance and/or early intervention for treatment-related cognitive effects.

Distribution of global health measures from routinely collected PROMIS surveys in patients with breast cancer or prostate cancer.

BACKGROUND: The collection of patient-reported outcomes (PROs) is an emerging priority internationally, guiding clinical care, quality improvement projects and research studies. After the deployment of Patient-Reported Outcomes Measurement Information System (PROMIS) surveys in routine outpatient workflows at an academic cancer center, electronic health record data were used to evaluate survey completion rates and self-reported global health measures across 2 tumor types: breast and prostate cancer. METHODS: This study retrospectively analyzed 11,657 PROMIS surveys from patients with breast cancer and 4411 surveys from patients with prostate cancer, and it calculated survey completion rates and global physical health (GPH) and global mental health (GMH) scores between 2013 and 2018. RESULTS: A total of 36.6% of eligible patients with breast cancer and 23.7% of patients with prostate cancer completed at least 1 survey, with completion rates lower among black patients for both tumor types (P < .05). The mean T scores (calibrated to a general population mean of 50) for GPH were 48.4 ± 9 for breast cancer and 50.6 ± 9 for prostate cancer, and the GMH scores were 52.7 ± 8 and 52.1 ± 9, respectively. GPH and GMH were frequently lower among ethnic minorities, patients without private health insurance, and those with advanced disease. CONCLUSIONS: This analysis provides important baseline data on patient-reported global health in breast and prostate cancer. Demonstrating that PROs can be integrated into clinical workflows, this study shows that supportive efforts may be needed to improve PRO collection and global health endpoints in vulnerable populations.

PSA Testing Use and Prostate Cancer Diagnostic Stage After the 2012 U.S. Preventive Services Task Force Guideline Changes.

BACKGROUND: Most patients with prostate cancer are diagnosed with low-grade, localized disease and may not require definitive treatment. In 2012, the U.S. Preventive Services Task Force (USPSTF) recommended against prostate cancer screening to address overdetection and overtreatment. This study sought to determine the effect of guideline changes on prostate-specific antigen (PSA) screening and initial diagnostic stage for prostate cancer. PATIENTS AND METHODS: A difference-in-differences analysis was conducted to compare changes in PSA screening (exposure) relative to cholesterol testing (control) after the 2012 USPSTF guideline changes, and chi-square test was used to determine whether there was a subsequent decrease in early-stage, low-risk prostate cancer diagnoses. Data were derived from a tertiary academic medical center's electronic health records, a national commercial insurance database (OptumLabs), and the SEER database for men aged ≥35 years before (2008-2011) and after (2013-2016) the guideline changes. RESULTS: In both the academic center and insurance databases, PSA testing significantly decreased for all men compared with the control. The greatest decrease was among men aged 55 to 74 years at the academic center and among those aged ≥75 years in the commercial database. The proportion of early-stage prostate cancer diagnoses (

Comparison of orthogonal NLP methods for clinical phenotyping and assessment of bone scan utilization among prostate cancer patients.

OBJECTIVE: Clinical care guidelines recommend that newly diagnosed prostate cancer patients at high risk for metastatic spread receive a bone scan prior to treatment and that low risk patients not receive it. The objective was to develop an automated pipeline to interrogate heterogeneous data to evaluate the use of bone scans using a two different Natural Language Processing (NLP) approaches. MATERIALS AND METHODS: Our cohort was divided into risk groups based on Electronic Health Records (EHR). Information on bone scan utilization was identified in both structured data and free text from clinical notes. Our pipeline annotated sentences with a combination of a rule-based method using the ConText algorithm (a generalization of NegEx) and a Convolutional Neural Network (CNN) method using word2vec to produce word embeddings. RESULTS: A total of 5500 patients and 369,764 notes were included in the study. A total of 39% of patients were high-risk and 73% of these received a bone scan; of the 18% low risk patients, 10% received one. The accuracy of CNN model outperformed the rule-based model one (F-measure = 0.918 and 0.897 respectively). We demonstrate a combination of both models could maximize precision or recall, based on the study question. CONCLUSION: Using structured data, we accurately classified patients' cancer risk group, identified bone scan documentation with two NLP methods, and evaluated guideline adherence. Our pipeline can be used to provide concrete feedback to clinicians and guide treatment decisions.

Oncologists' Views on Using Value to Guide Cancer Treatment Decisions.

OBJECTIVES: Cancer costs have increased substantially in the past decades, prompting specialty societies to urge oncologists to consider value in clinical decision making. Despite oncologists' crucial role in guiding cancer care, current literature is sparse with respect to the oncologists' views on value. Here, we evaluated oncologists perceptions of the use and measurement of value in cancer care. METHODS: We conducted in-depth, open-ended interviews with 31 US oncologists practicing nationwide in various environments. Oncologists discussed the definition, measurement, and implementation of value. Transcripts were analyzed using matrix and thematic analysis. RESULTS: Oncologists' definitions of value varied greatly. Some described versions of the standard health economic definition of value, that is, cost relative to health outcomes. Many others did not include cost in their definition of value. Oncologists considered patient goals and quality of life as important components of value that they perceived were missing from current value measurement. Oncologists prioritized a patient-centric view of value over societal or other perspectives. Oncologists were inclined to consider the value of a treatment only if they perceived treatment would pose a financial burden to patients. Oncologists had differing opinions regarding who should be responsible for determining whether care is low value but generally felt this should remain within the purview of the oncology community. CONCLUSIONS: Oncologists agreed that cost was an important issue, but disagreed about whether cost was involved in value as well as the role of value in guiding treatment. Better clarity and alignment on the definition of and appropriate way to measure value is critical to the success of efforts to improve value in cancer care.

Myeloid Growth Factors, Version 2.2017, NCCN Clinical Practice Guidelines in Oncology.

Myeloid growth factors (MGFs) are given as supportive care to patients receiving myelosuppressive chemotherapy to reduce the incidence of neutropenia. This selection from the NCCN Guidelines for MGFs focuses on the evaluation of regimen- and patient-specific risk factors for the development of febrile neutropenia (FN), the prophylactic use of MGFs for the prevention of chemotherapy-induced FN, and assessing the risks and benefits of MGF use in clinical practice.

Breast cancer treatment across health care systems: linking electronic medical records and state registry data to enable outcomes research.

BACKGROUND: Understanding of cancer outcomes is limited by data fragmentation. In the current study, the authors analyzed the information yielded by integrating breast cancer data from 3 sources: electronic medical records (EMRs) from 2 health care systems and the state registry. METHODS: Diagnostic test and treatment data were extracted from the EMRs of all patients with breast cancer treated between 2000 and 2010 in 2 independent California institutions: a community-based practice (Palo Alto Medical Foundation; "Community") and an academic medical center (Stanford University; "University"). The authors incorporated records from the population-based California Cancer Registry and then linked EMR-California Cancer Registry data sets of Community and University patients. RESULTS: The authors initially identified 8210 University patients and 5770 Community patients; linked data sets revealed a 16% patient overlap, yielding 12,109 unique patients. The percentage of all Community patients, but not University patients, treated at both institutions increased with worsening cancer prognostic factors. Before linking the data sets, Community patients appeared to receive less intervention than University patients (mastectomy: 37.6% vs 43.2%; chemotherapy: 35% vs 41.7%; magnetic resonance imaging: 10% vs 29.3%; and genetic testing: 2.5% vs 9.2%). Linked Community and University data sets revealed that patients treated at both institutions received substantially more interventions (mastectomy: 55.8%; chemotherapy: 47.2%; magnetic resonance imaging: 38.9%; and genetic testing: 10.9% [P < .001 for each 3-way institutional comparison]). CONCLUSIONS: Data linkage identified 16% of patients who were treated in 2 health care systems and who, despite comparable prognostic factors, received far more intensive treatment than others. By integrating complementary data from EMRs and population-based registries, a more comprehensive understanding of breast cancer care and factors that drive treatment use was obtained.

Comparative outcome of initial therapy for younger patients with mantle cell lymphoma: an analysis from the NCCN NHL Database.

Few randomized trials have compared therapies in mantle cell lymphoma (MCL), and the role of aggressive induction is unclear. The National Comprehensive Cancer Network (NCCN) Non-Hodgkin Lymphoma (NHL) Database, a prospective cohort study collecting clinical, treatment, and outcome data at 7 NCCN centers, provides a unique opportunity to compare the effectiveness of initial therapies in MCL. Patients younger than 65 diagnosed between 2000 and 2008 were included if they received RHCVAD (rituximab fractionated cyclophosphamide, vincristine, adriamycin, and dexamethasone), RCHOP+HDT/ASCR (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone + high-dose therapy/autologous stem cell rescue), RHCVAD+HDT/ASCR, or RCHOP. Clinical parameters were similar for patients treated with RHCVAD (n = 83, 50%), RCHOP+HDT/ASCR (n = 34, 20%), RCHOP (n = 29, 17%), or RHCVAD+HDT/ASCR (n = 21, 13%). Overall, 70 (42%) of the 167 patients progressed and 25 (15%) expired with a median follow-up of 33 months. There was no difference in progression-free survival (PFS) between aggressive regimens (P > .57), which all demonstrated superior PFS compared with RCHOP (P < .004). There was no difference in overall survival (OS) between the RHCVAD and RCHOP+HDT/ASCR (P = .98). RCHOP was inferior to RHCVAD and RCHOP+HDT/ASCR, which had similar PFS and OS. Despite aggressive regimens, the median PFS was 3 to 4 years. Future trials should focus on novel agents rather than comparing current approaches.

Computing the Cost of Care per Patient per Day for Patients With Metastatic Neuroendocrine Neoplasms.

PURPOSE: Patients with well-differentiated, low-grade metastatic neuroendocrine neoplasms (NENs) usually have a long median survival and require complex, expensive care over many years at multidisciplinary centers. The cost burden for patients and institutions serves as a barrier to care. Understanding the drivers of these costs and whether intense monitoring adds value will help to optimize value-based care. METHODS: We adapted the cost of care per patient per day (CCPD) validated methodology to measure cost while accounting for varying follow-up duration. We queried the Stanford NEN Database, which aggregates data from the electronic health record and other electronic sources, to study patients with metastatic NENs receiving regular care at Stanford. Current Procedural Terminology codes for services incurred during the monitoring period for each patient were mapped to the corresponding cost conversion factor and date in the Medicare fee schedule. RESULTS: Two hundred two patients between 2010 and 2017 were studied with a mean CCPD of $119.11 in US dollars (USD); NEN-specific systemic therapy made up 55% of this cost. Somatostatin analogs were the costliest systemic therapy. Systemic therapy was the driver of cost differences among patients with various primary tumor types, stage of disease, tumor differentiation and grade, and functional hormone status. Patients in the most expensive CCPD group did not have a significant survival benefit (P = .66). CONCLUSION: The CCPD methodology was effective in studying cancer care value in NENs. Systemic therapy, specifically somatostatin analogs, was the primary driver of cost, and intense monitoring and higher-cost care did not improve survival outcomes.

Long-Term Engagement of Patients With Advanced Cancer: Results From the EPAC Randomized Clinical Trial.

Importance: The long-term effect of interventions that assist patients with establishing their end-of-life care preferences among patients with cancer remain relatively unknown. Objective: To evaluate the association of a long-term intervention of a lay health worker-led advance care planning intervention among patients with advanced stages of cancer with overall survival and end-of-life health care use and costs. Design, Setting, and Participants: This follow-up study of the EPAC randomized clinical trial conducted between August 2013 and February 2015 used data from 9.4 years after the first patient was enrolled with a data cut-off date of February 1, 2023. Overall, 213 participants with stage 3 or 4 or recurrent cancer in the US Veterans Affairs Palo Alto Health Care System were included. Interventions: A 6-month lay health worker-led education and support intervention to assist patients with establishing their end-of-life preferences vs usual care. Main Outcomes and Measures: The outcomes of interest were overall survival, risk of death, restricted mean survival time, and palliative care, hospice, and acute care use in the final 30 days before death for participants who died. Results: Among 213 participants randomized and included in the intention-to-treat analysis, the mean (SD) age was 69.3 (9.1) years; 211 (99.1%) were male, 2 (0.90%) were female. There were no demographic or clinical characteristic imbalances at enrollment. As of February 1, 2023, 188 had died. The intervention group had a 25% reduction in risk of death (hazard ratio, 0.75; 95% CI, 0.56-0.98); more palliative care (44 [50.0%] vs 35 [35.0%]) and hospice use (64 [72.7%] vs 53 [53.0%]); and lower emergency department use (20 [22.7%] vs 47 [47.0%]), hospitalizations (17 [19.3%] vs 46 [46.0%]), and median (IQR) total health care costs (median [IQR], $1637 [$383-$9026] vs $18 520 [$4790-$50 729]) than control group participants. Conclusions and Relevance: The effects of the lay health worker-led intervention remain durable, with nearly complete follow-up, supporting integration into routine cancer care. Trial Registration: ClinicalTrials.gov Identifier: NCT02966509.

Predicting Depression Risk in Patients With Cancer Using Multimodal Data: Algorithm Development Study.

BACKGROUND: Patients with cancer starting systemic treatment programs, such as chemotherapy, often develop depression. A prediction model may assist physicians and health care workers in the early identification of these vulnerable patients. OBJECTIVE: This study aimed to develop a prediction model for depression risk within the first month of cancer treatment. METHODS: We included 16,159 patients diagnosed with cancer starting chemo- or radiotherapy treatment between 2008 and 2021. Machine learning models (eg, least absolute shrinkage and selection operator [LASSO] logistic regression) and natural language processing models (Bidirectional Encoder Representations from Transformers [BERT]) were used to develop multimodal prediction models using both electronic health record data and unstructured text (patient emails and clinician notes). Model performance was assessed in an independent test set (n=5387, 33%) using area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curve analysis to assess initial clinical impact use. RESULTS: Among 16,159 patients, 437 (2.7%) received a depression diagnosis within the first month of treatment. The LASSO logistic regression models based on the structured data (AUROC 0.74, 95% CI 0.71-0.78) and structured data with email classification scores (AUROC 0.74, 95% CI 0.71-0.78) had the best discriminative performance. The BERT models based on clinician notes and structured data with email classification scores had AUROCs around 0.71. The logistic regression model based on email classification scores alone performed poorly (AUROC 0.54, 95% CI 0.52-0.56), and the model based solely on clinician notes had the worst performance (AUROC 0.50, 95% CI 0.49-0.52). Calibration was good for the logistic regression models, whereas the BERT models produced overly extreme risk estimates even after recalibration. There was a small range of decision thresholds for which the best-performing model showed promising clinical effectiveness use. The risks were underestimated for female and Black patients. CONCLUSIONS: The results demonstrated the potential and limitations of machine learning and multimodal models for predicting depression risk in patients with cancer. Future research is needed to further validate these models, refine the outcome label and predictors related to mental health, and address biases across subgroups.

Evidence of compensatory neural hyperactivity in a subgroup of chemotherapy-treated breast cancer survivors and its association with brain aging.

Chemotherapy-related cognitive impairment (CRCI) remains poorly understood in terms of the mechanisms of cognitive decline. Neural hyperactivity has been reported on average in cancer survivors, but it is unclear which patients demonstrate this neurophenotype, limiting precision medicine in this population. We evaluated a retrospective sample of 80 breast cancer survivors and 80 non-cancer controls, age 35-73, for which we had previously identified and validated three data-driven, biological subgroups (biotypes) of CRCI. We measured neural activity using the z-normalized percent amplitude of fluctuation from resting state functional magnetic resonance imaging (MRI). We tested established, quantitative criteria to determine if hyperactivity can accurately be considered compensatory. We also calculated brain age gap by applying a previously validated algorithm to anatomic MRI. We found that neural activity differed across the three CRCI biotypes and controls (F = 13.5, p < 0.001), with Biotype 2 demonstrating significant hyperactivity compared to the other groups (p < 0.004, corrected), primarily in prefrontal regions. Alternatively, Biotypes 1 and 3 demonstrated significant hypoactivity (p < 0.02, corrected). Hyperactivity in Biotype 2 met several of the criteria to be considered compensatory. However, we also found a positive relationship between neural activity and brain age gap in these patients (r = 0.45, p = 0.042). Our results indicated that neural hyperactivity is specific to a subgroup of breast cancer survivors and, while it seems to support preserved cognitive function, it could also increase the risk of accelerated brain aging. These findings could inform future neuromodulatory interventions with respect to the risks and benefits of up or downregulation of neural activity.

Myeloid growth factors.

Febrile neutropenia, a common side effect of myelosuppressive chemotherapy in patients with cancer, can result in prolonged hospitalization and broad-spectrum antibiotic use, often prompting treatment delays or dose reductions of drug regimens. Prophylactic use of myeloid growth factors (mainly the colony-stimulating factors filgrastim and pegfilgrastim) in patients of heightened risk can reduce the severity and duration of febrile neutropenia. The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Myeloid Growth Factors provide recommendations on the use of these agents mainly in the oncology setting based on clinical evidence and expert consensus. This version includes revisions surrounding the issue of timing of pegfilgrastim administration. It also includes new sections on tbo-filgrastim, a recently approved agent that is biologically similar to filgrastim, and the role of myeloid growth factors in the hematopoietic cell transplant setting.

Predicting Depression Risk in Patients with Cancer Using Multimodal Data.

When patients with cancer develop depression, it is often left untreated. We developed a prediction model for depression risk within the first month after starting cancer treatment using machine learning and Natural Language Processing (NLP) models. The LASSO logistic regression model based on structured data performed well, whereas the NLP model based on only clinician notes did poorly. After further validation, prediction models for depression risk could lead to earlier identification and treatment of vulnerable patients, ultimately improving cancer care and treatment adherence.

Clinicopathologic features, patterns of recurrence, and survival among women with triple-negative breast cancer in the National Comprehensive Cancer Network.

BACKGROUND: The objective of this study was to describe clinicopathologic features, patterns of recurrence, and survival according to breast cancer subtype with a focus on triple-negative tumors. METHODS: In total, 15,204 women were evaluated who presented to National Comprehensive Cancer Network centers with stage I through III breast cancer between January 2000 and December 2006. Tumors were classified as positive for estrogen receptor (ER) and/or progesterone receptor (PR) (hormone receptor [HR]-positive) and negative for human epidermal growth factor receptor 2 (HER2); positive for HER2 and any ER or PR status (HER2-positive); or negative for ER, PR, and HER2 (triple-negative). RESULTS: Subtype distribution was triple-negative in 17% of women (n = 2569), HER2-positive in 17% of women (n = 2602), and HR-positive/HER2-negative in 66% of women (n = 10,033). The triple-negative subtype was more frequent in African Americans compared with Caucasians (adjusted odds ratio, 1.98; P < .0001). Premenopausal women, but not postmenopausal women, with high body mass index had an increased likelihood of having the triple-negative subtype (P = .02). Women with triple-negative cancers were less likely to present on the basis of an abnormal screening mammogram (29% vs 48%; P < .0001) and were more likely to present with higher tumor classification, but they were less likely to have lymph node involvement. Relative to HR-positive/HER2-negative tumors, triple-negative tumors were associated with a greater risk of brain or lung metastases; and women with triple-negative tumors had worse breast cancer-specific and overall survival, even after adjusting for age, disease stage, race, tumor grade, and receipt of adjuvant chemotherapy (overall survival: adjusted hazard ratio, 2.72; 95% confidence interval, 2.39-3.10; P < .0001). The difference in the risk of death by subtype was most dramatic within the first 2 years after diagnosis (overall survival for 0-2 years: OR, 6.10; 95% confidence interval, 4.81-7.74). CONCLUSIONS: Triple-negative tumors were associated with unique risk factors and worse outcomes compared with HR-positive/HER2-negative tumors.

The effect of age on delay in diagnosis and stage of breast cancer.

BACKGROUND: Young women with breast cancer are more likely to present with more advanced disease and are more likely to die as a result of breast cancer than their older counterparts. We sought to examine the relationship among young age (≤40 years), the likelihood of a delay in diagnosis, and stage. METHODS: We examined data from women with newly diagnosed stage I-IV breast cancer presenting to one of eight National Comprehensive Cancer Network centers in January 2000 to December 2007. Delay in diagnosis was defined as time from initial sign or symptom to breast cancer diagnosis >60 days. RESULTS: Among 21,818 women with breast cancer eligible for analysis, 2,445 were aged ≤40 years at diagnosis. Young women were not more likely to have a delay in diagnosis >60 days (odds ratio [OR], 1.08; 95% confidence interval [CI], 0.98-1.19) after adjustment for type of initial sign or symptom. Young women were only modestly more likely to present with higher stage disease after a similar adjustment (OR, 1.18; 95% CI, 1.07-1.31). Women presenting with symptomatic disease, more common in younger women, were more likely to have a delay in diagnosis (OR, 3.31; 95% CI, 3.08-3.56) and higher stage (OR, 4.31; 95% CI 4.05-4.58). CONCLUSION: Young age is not an independent predictor of delay in diagnosis of breast cancer and only modestly is associated with higher stage disease. Presenting with symptoms of breast cancer predicts delay and higher stage at diagnosis.

Time to diagnosis and breast cancer stage by race/ethnicity.

We examined differences in time to diagnosis by race/ethnicity, the relationship between time to diagnosis and stage, and the extent to which it explains differences in stage at diagnosis across racial/ethnic groups. Our analytic sample includes 21,427 non-Hispanic White (White), Hispanic, non-Hispanic Black (Black) and non-Hispanic Asian/Pacific Islander (Asian) women diagnosed with stage I to IV breast cancer between January 1, 2000 and December 31, 2007 at one of eight National Comprehensive Cancer Network centers. We measured time from initial abnormal mammogram or symptom to breast cancer diagnosis. Stage was classified using AJCC criteria. Initial sign of breast cancer modified the association between race/ethnicity and time to diagnosis. Among symptomatic women, median time to diagnosis ranged from 36 days among Whites to 53.6 for Blacks. Among women with abnormal mammograms, median time to diagnosis ranged from 21 days among Whites to 29 for Blacks. Blacks had the highest proportion (26 %) of Stage III or IV tumors. After accounting for time to diagnosis, the observed increased risk of stage III/IV breast cancer was reduced from 40 to 28 % among Hispanics and from 113 to 100 % among Blacks, but estimates remained statistically significant. We were unable to fully account for the higher proportion of late-stage tumors among Blacks. Blacks and Hispanics experienced longer time to diagnosis than Whites, and Blacks were more likely to be diagnosed with late-stage tumors. Longer time to diagnosis did not fully explain differences in stage between racial/ethnicity groups.