Mapping the Anatomy of the Human Lymphatic System.

BACKGROUND: While substantial anatomical study has been pursued throughout the human body, anatomical study of the human lymphatic system remains in its infancy. For microsurgeons specializing in lymphatic surgery, a better command of lymphatic anatomy is needed to further our ability to offer surgical interventions with precision. In an effort to facilitate the dissemination and advancement of human lymphatic anatomy knowledge, our teams worked together to create a map. The aim of this paper is to present our experience in mapping the anatomy of the human lymphatic system. METHODS: Three steps were followed to develop a modern map of the human lymphatic system: (1) identifying our source material, which was "Anatomy of the human lymphatic system," published by Rouvière and Tobias (1938), (2) choosing a modern platform, the Miro Mind Map software, to integrate the source material, and (3) transitioning our modern platform into The Human BioMolecular Atlas Program (HuBMAP). RESULTS: The map of lymphatic anatomy based on the Rouvière textbook contained over 900 data points. Specifically, the map contained 404 channels, pathways, or trunks and 309 lymph node groups. Additionally, lymphatic drainage from 165 distinct anatomical regions were identified and integrated into the map. The map is being integrated into HuBMAP by creating a standard data format called an Anatomical Structures, Cell Types, plus Biomarkers table for the lymphatic vasculature, which is currently in the process of construction. CONCLUSION: Through a collaborative effort, we have developed a unified and centralized source for lymphatic anatomy knowledge available to the entire scientific community. We believe this resource will ultimately advance our knowledge of human lymphatic anatomy while simultaneously highlighting gaps for future research. Advancements in lymphatic anatomy knowledge will be critical for lymphatic surgeons to further refine surgical indications and operative approaches.

The power of partnership: Exploring collaboration dynamics in U.S. transplant research.

INTRODUCTION: Collaboration is one of the hallmarks of academic research. This study analyzes collaboration patterns in U.S. transplant research, examining publication trends, productive institutions, co-authorship networks, and citation patterns in high-impact transplant journals. METHODS: 4,265 articles published between 2012 and 2021 were analyzed using scientometric tools, logistic regression, VantagePoint software, and Gephi software for network visualization. RESULTS: 16,003 authors from 1,011 institutions and 59 countries were identified, with Harvard, Johns Hopkins, and University of Pennsylvania contributing the most papers. Odds of international collaboration significantly increased over time (OR 1.03; p â€‹= â€‹0.040), while odds of citation in single-institution collaborations decreased (OR 0.99; p â€‹= â€‹0.016). Five major scientific communities and central institutions (Harvard University and University of Pittsburgh) connecting them were identified, revealing interconnected research clusters. CONCLUSIONS: Collaboration enhances knowledge exchange and research productivity, with an increasing trend of institutional and international collaboration in U.S. transplant research. Understanding this community is essential for promoting research impact and forming strategic partnerships.

A broadly applicable approach to enrich electronic-health-record cohorts by identifying patients with complete data: a multisite evaluation.

OBJECTIVE: Patients who receive most care within a single healthcare system (colloquially called a "loyalty cohort" since they typically return to the same providers) have mostly complete data within that organization's electronic health record (EHR). Loyalty cohorts have low data missingness, which can unintentionally bias research results. Using proxies of routine care and healthcare utilization metrics, we compute a per-patient score that identifies a loyalty cohort. MATERIALS AND METHODS: We implemented a computable program for the widely adopted i2b2 platform that identifies loyalty cohorts in EHRs based on a machine-learning model, which was previously validated using linked claims data. We developed a novel validation approach, which tests, using only EHR data, whether patients returned to the same healthcare system after the training period. We evaluated these tools at 3 institutions using data from 2017 to 2019. RESULTS: Loyalty cohort calculations to identify patients who returned during a 1-year follow-up yielded a mean area under the receiver operating characteristic curve of 0.77 using the original model and 0.80 after calibrating the model at individual sites. Factors such as multiple medications or visits contributed significantly at all sites. Screening tests' contributions (eg, colonoscopy) varied across sites, likely due to coding and population differences. DISCUSSION: This open-source implementation of a "loyalty score" algorithm had good predictive power. Enriching research cohorts by utilizing these low-missingness patients is a way to obtain the data completeness necessary for accurate causal analysis. CONCLUSION: i2b2 sites can use this approach to select cohorts with mostly complete EHR data.

Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies.

Although randomized controlled trials (RCTs) are the gold standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data has been vital in postapproval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of real-world data is electronic health records (EHRs), which contain detailed information on patient care in both structured (eg, diagnosis codes) and unstructured (eg, clinical notes and images) forms. Despite the granularity of the data available in EHRs, the critical variables required to reliably assess the relationship between a treatment and clinical outcome are challenging to extract. To address this fundamental challenge and accelerate the reliable use of EHRs for RWE, we introduce an integrated data curation and modeling pipeline consisting of 4 modules that leverage recent advances in natural language processing, computational phenotyping, and causal modeling techniques with noisy data. Module 1 consists of techniques for data harmonization. We use natural language processing to recognize clinical variables from RCT design documents and map the extracted variables to EHR features with description matching and knowledge networks. Module 2 then develops techniques for cohort construction using advanced phenotyping algorithms to both identify patients with diseases of interest and define the treatment arms. Module 3 introduces methods for variable curation, including a list of existing tools to extract baseline variables from different sources (eg, codified, free text, and medical imaging) and end points of various types (eg, death, binary, temporal, and numerical). Finally, module 4 presents validation and robust modeling methods, and we propose a strategy to create gold-standard labels for EHR variables of interest to validate data curation quality and perform subsequent causal modeling for RWE. In addition to the workflow proposed in our pipeline, we also develop a reporting guideline for RWE that covers the necessary information to facilitate transparent reporting and reproducibility of results. Moreover, our pipeline is highly data driven, enhancing study data with a rich variety of publicly available information and knowledge sources. We also showcase our pipeline and provide guidance on the deployment of relevant tools by revisiting the emulation of the Clinical Outcomes of Surgical Therapy Study Group Trial on laparoscopy-assisted colectomy versus open colectomy in patients with early-stage colon cancer. We also draw on existing literature on EHR emulation of RCTs together with our own studies with the Mass General Brigham EHR.

Antiplatelet medications and intracranial hemorrhage in patients with primary brain tumors.

BACKGROUND: Spontaneous intracranial hemorrhage (ICH) is a frequent and severe consequence of primary brain tumors. The safety of antiplatelet medications in this patient population is undefined. OBJECTIVE: The primary objective was to determine whether antiplatelet medications are associated with an increased risk of ICH in patients with primary brain tumors. PATIENTS/METHODS: We performed a matched, retrospective cohort study of patients with the diagnosis of primary brain tumor treated at our institution between 2010 and 2021. Radiographic images of all potential ICH events underwent blinded review. The primary end point of the study was the cumulative incidence of ICH at 1 year after tumor diagnosis. RESULTS AND CONCLUSIONS: A total of 387 patients with primary brain tumors were included in the study population (130 exposed to antiplatelet agents, 257 not exposed). The most common malignancy was glioblastoma (n = 256, 66.1%). Among the intervention cohort, 119 patients received aspirin monotherapy. The cumulative incidence of any ICH at 1 year was 11.0% (95% CI, 5.3-16.6) in those receiving antiplatelet medications and 13.0% (95% CI, 8.5-17.6) in those not receiving antiplatelet medications (Gray test, p = 0.6). The cumulative incidence of major ICH was similar between the cohorts (3.3% in antiplatelet cohort vs 2.9% in control cohort, p = 1.0). This study did not identify an increased incidence of ICH in patients with primary brain tumors exposed to antiplatelet medications.

Antiplatelet medications and risk of intracranial hemorrhage in patients with metastatic brain tumors.

Although intracranial hemorrhage (ICH) is frequent in the setting of brain metastases, there are limited data on the influence of antiplatelet agents on the development of brain tumor-associated ICH. To evaluate whether the administration of antiplatelet agents increases the risk of ICH, we performed a matched cohort analysis of patients with metastatic brain tumors with blinded radiology review. The study population included 392 patients with metastatic brain tumors (134 received antiplatelet agents and 258 acted as controls). Non-small cell lung cancer was the most common malignancy in the cohort (74.0%), followed by small cell lung cancer (9.9%), melanoma (4.6%), and renal cell cancer (4.3%). Among those who received an antiplatelet agent, 86.6% received aspirin alone and 23.1% received therapeutic anticoagulation during the study period. The cumulative incidence of any ICH at 1 year was 19.3% (95% CI, 14.1-24.4) in patients not receiving antiplatelet agents compared with 22.5% (95% CI, 15.2-29.8; P = .22, Gray test) in those receiving antiplatelet agents. The cumulative incidence of major ICH was 5.4% (95% CI, 2.6-8.3) among controls compared with 5.5% (95% CI, 1.5-9.5; P = .80) in those exposed to antiplatelet agents. The combination of anticoagulation plus antiplatelet agents did not increase the risk of major ICH. The use of antiplatelet agents was not associated with an increase in the incidence, size, or severity of ICH in the setting of brain metastases.

Anticoagulation after intracranial hemorrhage in brain tumors: Risk of recurrent hemorrhage and venous thromboembolism.

BACKGROUND: Intracranial hemorrhage (ICH) is a common and often devastating outcome in patients with brain tumors. Despite this, there is little evidence to guide anticoagulation management following an initial ICH event. OBJECTIVES: To analyze the risk of recurrent hemorrhagic and thrombotic outcomes after an initial ICH event in patients with brain tumors and prior venous thromboembolism (VTE). PATIENTS AND METHODS: A retrospective cohort study was performed. Radiographic images obtained after initial ICH were reviewed for the primary outcomes of recurrent ICH and VTE. RESULTS AND CONCLUSIONS: A total of 79 patients with brain tumors who developed ICH on anticoagulation for VTE were analyzed. Fifty-four patients (68.4%) restarted anticoagulation following ICH. The cumulative incidence of recurrent ICH at 1 year was 6.1% (95% confidence interval [CI], 1.5-15.3) following reinitiation of anticoagulation. Following a major ICH (defined as an ICH >10 mL in size, causing symptoms, or requiring intervention), the rate of recurrent ICH upon reexposure to anticoagulation was 14.5% (95% CI, 2.1-38.35), whereas the rate of recurrent ICH following smaller ICH was 2.6% (95% CI, 0.2%-12.0%). Mortality following a recurrent ICH on anticoagulation was 67% at 30 days. The cumulative incidence of recurrent VTE was significantly lower in the restart cohort compared to patients who did not restart anticoagulation (8.1% vs 35.3%; P = .003). We conclude that resumption of anticoagulation is lowest among patients with metastatic brain tumors with small initial ICH. Following an initial major ICH, resumption of anticoagulation was associated with a high rate of recurrent ICH.

Predicting the higher rate of intracranial hemorrhage in glioma patients receiving therapeutic enoxaparin.

Venous thromboembolism occurs in up to one-third of patients with primary brain tumors. Spontaneous intracranial hemorrhage (ICH) is also a frequent occurrence in these patients, but there is limited data on the safety of therapeutic anticoagulation. To determine the rate of ICH in patients treated with enoxaparin, we performed a matched, retrospective cohort study with blinded radiology review for 133 patients with high-grade glioma. After diagnosis of glioma, the cohort that received enoxaparin was 3 times more likely to develop a major ICH than those not treated with anticoagulation (14.7% vs 2.5%; P = .036; hazard ratio [HR], 3.37; 95% confidence interval [CI], 1.02-11.14). When enoxaparin was analyzed as a time-varying covariate, anticoagulation was associated with a >13-fold increased risk of hemorrhage (HR, 13.26; 95% CI, 3.33-52.85; P < .0001). Overall survival was significantly shorter for patients who suffered a major ICH on enoxaparin compared with patients not receiving anticoagulation (3.3 vs 10.2 months; log-rank P = .012). We applied a validated ICH prediction risk score PANWARDS (platelets, albumin, no congestive heart failure, warfarin, age, race, diastolic blood pressure, stroke), and observed that all major ICHs on enoxaparin occurred in the setting of a PANWARDS score ≥25, corresponding with a sensitivity of 100% (95% CI, 63% to 100%) and a specificity of 40% (95% CI, 25% to 56%). We conclude that caution is warranted when considering therapeutic anticoagulation in patients with high-grade gliomas given the increased risk of ICH and poor prognosis after a major hemorrhage on anticoagulation. The PANWARDS score may assist clinicians in identifying the patients at greatest risk of suffering a major intracranial hemorrhage with anticoagulation.

Intracranial hemorrhage in patients with brain metastases treated with therapeutic enoxaparin: a matched cohort study.

Venous thromboembolism occurs frequently in patients with cancer who have brain metastases, but there is limited evidence supporting the safety of therapeutic anticoagulation. To assess the risk for intracranial hemorrhage associated with the administration of therapeutic doses of low-molecular-weight heparin, we performed a matched, retrospective cohort study of 293 patients with cancer with brain metastases (104 with therapeutic enoxaparin and 189 controls). A blinded review of radiographic imaging was performed, and intracranial hemorrhages were categorized as trace, measurable, and significant. There were no differences observed in the cumulative incidence of intracranial hemorrhage at 1 year in the enoxaparin and control cohorts for measurable (19% vs 21%; Gray test, P = .97; hazard ratio, 1.02; 90% confidence interval [CI], 0.66-1.59), significant (21% vs 22%; P = .87), and total (44% vs 37%; P = .13) intracranial hemorrhages. The risk for intracranial hemorrhage was fourfold higher (adjusted hazard ratio, 3.98; 90% CI, 2.41-6.57; P < .001) in patients with melanoma or renal cell carcinoma (N = 60) than lung cancer (N = 153), but the risk was not influenced by the administration of enoxaparin. Overall survival was similar for the enoxaparin and control cohorts (8.4 vs 9.7 months; Log-rank, P = .65). We conclude that intracranial hemorrhage is frequently observed in patients with brain metastases, but that therapeutic anticoagulation does not increase the risk for intracranial hemorrhage.

Considering context in academic medicine: differences in demographic and professional characteristics and in research productivity and advancement metrics across seven clinical departments.

PURPOSE: To understand the disciplinary contexts in which faculty work, the authors examined demographics, professional characteristics, research productivity, and advancement across seven clinical departments at Harvard Medical School (HMS) and nationally. METHOD: HMS analyses included faculty from seven clinical departments-anesthesiology, medicine, neurology, pediatrics, psychiatry, radiology, and surgery-in May 2011 (N = 7,304). National analyses included faculty at 141 U.S. medical schools in the same seven departments as of December 31, 2011 (N = 91,414). The authors used chi-square and Wilcoxon Mann-Whitney tests to compare departmental characteristics. RESULTS: Heterogeneity in demographics, professional characteristics, and advancement across departments was observed in HMS and national data. At HMS, psychiatry had the highest percentage of underrepresented minority faculty at 6.6% (75/1,139). In anesthesiology, 24.2% (128/530) of faculty were Asian, whereas in psychiatry only 7.9% (90/1,139) were (P < .0001). Female faculty were the majority in pediatrics and psychiatry, whereas in surgery 26.3% (172/654) of the faculty were female (P < .0001). At HMS, surgery, radiology, and neurology had the shortest median times to promotion and the highest median number of publications, H-index, and second-degree centrality. Neurology also had the highest percentage of faculty who had been principal investigators on a National Institutes of Health-funded grant. CONCLUSIONS: There were differences in demographics, professional characteristics, and advancement across clinical departments at HMS and nationally. The context in which faculty work, of which department is a proxy, should be accounted for in research on faculty career outcomes and diversity inclusion in academic medicine.

SHRINE: enabling nationally scalable multi-site disease studies.

Results of medical research studies are often contradictory or cannot be reproduced. One reason is that there may not be enough patient subjects available for observation for a long enough time period. Another reason is that patient populations may vary considerably with respect to geographic and demographic boundaries thus limiting how broadly the results apply. Even when similar patient populations are pooled together from multiple locations, differences in medical treatment and record systems can limit which outcome measures can be commonly analyzed. In total, these differences in medical research settings can lead to differing conclusions or can even prevent some studies from starting. We thus sought to create a patient research system that could aggregate as many patient observations as possible from a large number of hospitals in a uniform way. We call this system the 'Shared Health Research Information Network', with the following properties: (1) reuse electronic health data from everyday clinical care for research purposes, (2) respect patient privacy and hospital autonomy, (3) aggregate patient populations across many hospitals to achieve statistically significant sample sizes that can be validated independently of a single research setting, (4) harmonize the observation facts recorded at each institution such that queries can be made across many hospitals in parallel, (5) scale to regional and national collaborations. The purpose of this report is to provide open source software for multi-site clinical studies and to report on early uses of this application. At this time SHRINE implementations have been used for multi-site studies of autism co-morbidity, juvenile idiopathic arthritis, peripartum cardiomyopathy, colorectal cancer, diabetes, and others. The wide range of study objectives and growing adoption suggest that SHRINE may be applicable beyond the research uses and participating hospitals named in this report.

An online evidence-based decision support system for distinguishing benign from malignant vertebral compression fractures by magnetic resonance imaging feature analysis.

Decision support systems have been used to promote the practice of evidence-based medicine. Computer-assisted diagnosis can serve as one element of evidence-based radiology. One area where such tools may provide benefit is analysis of vertebral compression fractures (VCFs), which can be a challenge in MRI interpretation. VCFs may be benign or malignant in etiology, and several MRI features may help to make this important distinction. We describe a web-based decision support system for discriminating benign from malignant VCFs as a prototype for a more general diagnostic decision support framework for radiologists. The system has three components: a feature checklist with an image gallery derived from proven reference cases, a prediction model, and a reporting mechanism. The website allows users to input the findings for a case to be interpreted using a structured feature checklist. The image gallery complements the checklist, for clarity and training purposes. The input from the checklist is then used to calculate the likelihood of malignancy by a logistic regression prediction model. Standardized report text is generated that summarizes pertinent positive and negative findings. This computer-assisted diagnosis system demonstrates the integration of three areas where diagnostic decision support can aid radiologists: first, in image interpretation, through feature checklists and illustrative image galleries; second, in feature-based prediction modeling; and third, in structured reporting. We present a diagnostic decision support tool that provides radiologists with evidence-based guidance for discriminating benign from malignant VCF. This model may be useful in other difficult-diagnosis situations and requires further clinical testing.

Rosiglitazone and delayed onset of proliferative diabetic retinopathy.

OBJECTIVE: To evaluate whether rosiglitazone maleate, an oral peroxisome-proliferating activated receptor gamma agonist and oral insulin sensitizing agent with potential antiangiogenic activity, delays onset of proliferative diabetic retinopathy (PDR). METHODS: Longitudinal medical record review of all patients treated with rosiglitazone receiving both medical and ophthalmic care at the Joslin Diabetes Center from May 1, 2002, to May 31, 2003 (N = 124), and matched control patients not taking a glitazone drug (N = 158). The mean duration of follow-up was 2.8 years (range, 0.3-9.0 years). RESULTS: Baseline characteristics and final hemoglobin A(1c) values (7.6% and 7.8%, respectively) were similar in the rosiglitazone and control groups (P = .10). In eyes with severe nonproliferative diabetic retinopathy at baseline (rosiglitazone group, 14 eyes; control group, 24 eyes), progression to PDR over 3 years occurred in 19.2% in the rosiglitazone group and 47.4% in the control group, representing a 59% relative risk reduction (Wilcoxon, P = .045; log-rank, P = .059). Fewer eyes in the rosiglitazone group experienced 3 or more lines of visual acuity loss (P = .03). The incidence of diabetic macular edema was similar in both groups. CONCLUSIONS: Rosiglitazone may delay the onset of PDR, possibly because of its antiangiogenic activity. Future clinical investigations should consider analysis of this potential benefit along with ongoing evaluation of potential cardiac risk in studies where the risk-benefit profiles are deemed appropriate.

Short-term menopausal hormone therapy for symptom relief: an updated decision model.

BACKGROUND: Hormone therapy (HT) provides the most effective relief of menopausal symptoms. This therapy is associated with a decreased risk of osteoporosis and colorectal cancer but increased risks of cardiovascular disease (CVD), venous thrombosis, and breast cancer. Our objective was to identify which women should benefit from short-term HT by exploring the trade-off between symptom relief and risks of inducing disease. METHODS: A Markov model simulates the effect of short-term (2 years) estrogen and progestin HT on life expectancy and quality-adjusted life expectancy (QALE) among 50-year-old menopausal women with intact uteri, using findings from the Women's Health Initiative. Quality-of-life (QOL) utility scores were derived from the literature. We assumed HT-affected QOL only during perimenopause, when it reduced symptoms by 80%. RESULTS: Among asymptomatic women, short-term HT was associated with net losses in life expectancy and QALE of 1 to 3 months, depending on CVD risk. Women with mild or severe menopausal symptoms gained 3 to 4 months or 7 to 8 months of QALE, respectively. Among women at low risk for CVD, HT extended QALE if menopausal symptoms lowered QOL by as little as 4%. Among women at elevated CVD risk, HT extended QALE only if symptoms lowered QOL by at least 12%. CONCLUSIONS: Hormone therapy is associated with losses in survival but gains in QALE for women with menopausal symptoms. Women expected to benefit from short-term HT can be identified by the severity of their menopausal symptoms and CVD risk.

Pathogen discovery from human tissue by sequence-based computational subtraction.

We have recently reported a new pathogen discovery approach, "computational subtraction". With this approach, non-human transcripts are detected by sequencing cDNA libraries from infected tissue and eliminating those transcripts that match the human genome. We show now that this method is experimentally feasible. We generated a cDNA library from a tissue sample of post-transplant lymphoproliferative disorder (PTLD). 27,840 independent cDNA sequences were filtered by computational subtraction against the known human sequence to identify 32 nonmatching transcripts. Of these, 22 (0.1%) were found to be amplifiable from both infected and noninfected samples and were inferred to be human DNA not yet contained in the available human genome sequence. The remaining 10 sequences could be amplified only from Epstein-Barr virus (EBV)-infected tissues. All 10 corresponded to the known EBV sequence. This proof-of-principle experiment demonstrates that computational subtraction can detect pathogenic microbes in primary human-diseased tissue.

Building an asynchronous web-based tool for machine learning classification.

Various unsupervised and supervised learning methods including support vector machines, classification trees, linear discriminant analysis and nearest neighbor classifiers have been used to classify high-throughput gene expression data. Simpler and more widely accepted statistical tools have not yet been used for this purpose, hence proper comparisons between classification methods have not been conducted. We developed free software that implements logistic regression with stepwise variable selection as a quick and simple method for initial exploration of important genetic markers in disease classification. To implement the algorithm and allow our collaborators in remote locations to evaluate and compare its results against those of other methods, we developed a user-friendly asynchronous web-based application with a minimal amount of programming using free, downloadable software tools. With this program, we show that classification using logistic regression can perform as well as other more sophisticated algorithms, and it has the advantages of being easy to interpret and reproduce. By making the tool freely and easily available, we hope to promote the comparison of classification methods. In addition, we believe our web application can be used as a model for other bioinformatics laboratories that need to develop web-based analysis tools in a short amount of time and on a limited budget.

Predicting the survival of patients with breast carcinoma using tumor size.

BACKGROUND: Tumor size has long been recognized as the strongest predictor of the outcome of patients with invasive breast carcinoma, although it has not been settled whether the correlation between tumor size and the chance of death is independent of the method of detection, nor is it clear how tumor size at the time of treatment may be translated into a specific expectation of survival. In this report, the authors provide such a method. METHODS: A Kaplan-Meier survival analysis was carried out for a population of 1352 women with invasive breast carcinoma who were treated at the Van Nuys Breast Center between 1966 and 1990, and the data were analyzed together with survival data published by others. RESULTS: The authors found that the survival of patients with invasive breast carcinoma was a direct function of tumor size, independent of the method of detection. The results showed that the correlation between tumor size and survival was well fit by a simple equation, with which survival predictions could be made from information on tumor size. For example, a comparison of three large populations studied over the last 5 decades revealed a marked improvement (approximately 35% absolute) in the survival of patients with invasive breast carcinoma diagnosed on clinical grounds that could be ascribed to a reduction in tumor size. However, the capacity of screening mammography to find smaller tumors remains the best way reduce breast carcinoma deaths, with the potential for adding an additional approximately 20% absolute reduction in breast carcinoma deaths. The mathematic correlation between tumor size and survival is consistent with a biologic mechanism in which lethal distant metastasis occurs by discrete events of spread such that, for every invasive breast carcinoma cell in the primary tumor at the time of surgery, there is approximately a 1-in-1-billion chance that a lethal distant metastasis has formed. CONCLUSIONS: The correlation between tumor size and lethality is well captured by a simple equation that is consistent with breast carcinoma death as the result of discrete events of cellular spread occurring with small but definable probabilities.

The pattern of breast cancer screening utilization and its consequences.

BACKGROUND: The objective of this study was to describe the pattern of screening utilization and its consequences in terms of tumor size and time of tumor appearance of invasive breast carcinoma among a population of women who were examined at a large service screening/diagnostic program over the last decade. METHODS: Utilization of mammography was assessed from a population of 59,899 women who received 196,891 mammograms at the Massachusetts General Hospital Breast Imaging Division from January 1, 1990 to March 1, 1999, among which 604 invasive breast tumors were found. Two hundred six invasive, clinically detected tumors also were seen during this period among women who had no record of a previous mammogram. Additional information was available on screening of women from March 1, 1999 to June 1, 2001. RESULTS: Fifty percent of the women who used screening did not begin until the age of 50 years, although 25% of the invasive breast tumors were found in women age < 50 years. Relatively few of the women who used screening returned promptly for their annual examinations; by 1.5 years, only 50% had returned. Approximately 25% of the invasive breast tumors were found in women for whom there was no record of a previous screening mammogram, and these tumors were larger (median, 15 mm) than the screen-detected tumors (median, 10 mm). Approximately 30% of the 604 invasive breast tumors in the screening population were found on nonmammographic grounds, and they also were larger (median, 15 mm) than the screen-detected tumors (median, 10 mm). However, only 3% of these 604 tumors were found by nonmammographic criteria within 6 months of the previous negative examination, and only 12% were found within 1 year. By back calculating the likely size of each of these tumors at the time of the negative mammogram, it could be seen that most tumors probably emerged as larger, palpable masses not because they were missed at the previous negative mammogram, because most were too small then to have been detected, but because too much time had been allowed to pass. CONCLUSIONS: Far too many women did not comply with the American Cancer Society recommendation of prompt annual screening from the age of 40 years. Consequently, almost 50% of the invasive tumors emerged as larger and, thus, potentially more lethal, palpable masses.