scholarly journals Estimating the Annual Volume of Hematologic Cancer Cases per Hematologist-Oncologist in the United States: Are We Treating Rare Cancers Too Rarely?

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3297-3297
Author(s):  
Aishwarya Ravindran ◽  
Wilson I. Gonsalves ◽  
Shahrukh K. Hashmi ◽  
Prashant Kapoor ◽  
Ariela L. Marshall ◽  
...  
Keyword(s):  
Hodgkin Lymphoma ◽  
Myeloid Leukemia ◽  
Myeloproliferative Neoplasms ◽  
The United States ◽  
Lymphocytic Leukemia ◽  
Annual Number ◽  
Non Hodgkin Lymphoma ◽  
Hematologic Cancer ◽  
Outcome Relationship ◽  
The Us

Abstract BACKGROUND: While hematologic cancers comprise only 10% of all malignancies, they are divided into >100 distinct World Health Organization subtypes. It is known that higher volume of care is generally associated with better clinical outcomes. However, such a volume-outcome relationship in the medical management of hematologic cancers has not been rigorously explored. The American Society of Clinical Oncology (ASCO) National Census of Oncology Practices shows that the majority of hematologist-oncologists in the United States (US) have a combined hematology-oncology practice (J Oncol Pract 2013). In this study, we estimated the annual number of new and established patients with major hematologic cancers seen on average by a hematologist-oncologist in the US. METHODS: We estimated the number of hematologist-oncologists working in the US using the ASCO workforce information system data from 2011. We utilized statistics from the Surveillance Epidemiology and End Results (SEER) Program to determine the incidence and 37-year limited prevalence of hematologic cancers in 2011. We used 'first malignant tumor per site' statistics as the tumor inclusion method. For potentially curable hematologic cancers (acute lymphocytic leukemia, acute myeloid leukemia, Burkitt lymphoma, diffuse large b-cell lymphoma, Hodgkin lymphoma, and marginal zone lymphoma), we used the estimated 1-5 year survival rates from SEER and excluded patients who survived >5 years, since relapses are rare afterwards. Because prevalence estimates of chronic myelomonocytic leukemia, myelodysplastic syndromes, and certain subtypes of non-Hodgkin lymphoma are unavailable, we were unable to calculate the number of annual established cases. For myeloproliferative neoplasms, we obtained the prevalence estimate from Mehta J, et al (Leuk Lymphoma 2014). We derived the distribution of major non-Hodgkin lymphoma subtypes from the National Cancer Data Base (NCDB) Participant User File. RESULTS: The ASCO workforce information reported a total of 13,084 hematologist-oncologists working in the US in 2011. The Table summarizes the average number of specific hematologic cancer cases seen per hematologist-oncologist in 2011. CONCLUSION: Hematologic cancers are relatively rare but complex. In the US, a hematologist-oncologist on average cares for only 1-2 new patients of any subtype of hematologic cancers annually. The number of established patients is correspondingly low. These numbers are expected to vary by practice setting and disease specialization. As the diagnosis and management of hematologic cancers becomes more sophisticated, future research should explore the potential of a volume to clinical outcome relationship for these providers. Table. Hematologic Cancer Average Annual Number of Cases per Hematologist-Oncologist in the US New Cases Established Cases All Cases Acute lymphocytic leukemia 0.4 1.4 1.8 Acute myeloid leukemia 1 1.5 2.5 Chronic lymphocytic leukemia 1.1 10.7 11.8 Chronic myeloid leukemia 0.4 2.7 3.1 Chronic myelomonocytic leukemia 0.1 - - Hodgkin lymphoma 0.7 2.7 3.4 Multiple myeloma 1.6 6.3 7.9 Myelodysplastic syndromes 1.2 - - Myeloproliferative neoplasms 0.6 22.2 22.8 Non-Hodgkin lymphoma 5.1 - - Anaplastic large cell 0.1 - - Burkitt 0.1 0.3 0.4 Diffuse large B-cell 2 6 8 Follicular 1.1 - - Hairy cell leukemia 0.1 - - Lymphoplasmacytic 0.1 - - Mantle-cell 0.3 - - Marginal zone 0.5 2.1 2.6 Peripheral T-cell, not otherwise specified 0.1 - - Disclosures No relevant conflicts of interest to declare.

Malignant Hematology

2012 ◽  
Author(s):  
David P. Steensma
Keyword(s):  
Hodgkin Lymphoma ◽  
Myeloid Leukemia ◽  
Myeloproliferative Neoplasms ◽  
Lymphocytic Leukemia ◽  
Hematologic Neoplasms ◽  
Small Lymphocytic Lymphoma ◽  
Non Hodgkin Lymphoma ◽  
Myeloid Neoplasms ◽  
Plasma Cell Disorders ◽  
Heavy Chain Disease

The hematologic neoplasms include lymphoproliferative disorders (eg, chronic lymphocytic leukemia [CLL]/small lymphocytic lymphoma [SLL], large granular lymphocyte leukemia, hairy cell leukemia [HCL], Hodgkin lymphoma, non-Hodgkin lymphoma), plasma cell disorders (multiple myeloma, light chain amyloidosis, Waldenström macroglobulinemia, POEMS syndrome, heavy chain disease, plasmacytoma), chronic myeloid neoplasms (chronic myeloid leukemia, the BCR/ABL-negative myeloproliferative neoplasms, myelodysplastic syndromes), and acute leukemia (acute myeloid leukemia, acute lymphocytic leukemia). In addition, clonal but not overtly malignant conditions are common in the general population, including monoclonal gammopathy of undetermined significance (MGUS) and monoclonal B lymphocytosis (MBL).

scholarly journals Racial and ethnic disparities in hematologic malignancies

Blood ◽  
2017 ◽  
Vol 130 (15) ◽  
pp. 1699-1705 ◽  
Author(s):  
Kedar Kirtane ◽  
Stephanie J. Lee
Keyword(s):  
Hodgkin Lymphoma ◽  
Myeloid Leukemia ◽  
Myeloproliferative Neoplasms ◽  
Ethnic Disparities ◽  
Hematologic Malignancies ◽  
Lymphocytic Leukemia ◽  
Racial And Ethnic Disparities ◽  
Future Directions ◽  
Non Hodgkin Lymphoma ◽  
Solid Malignancies

Abstract Racial and ethnic disparities in patients with solid malignancies have been well documented. Less is known about these disparities in patients with hematologic malignancies. With the advent of novel chemotherapeutics and targeted molecular, cellular, and immunologic therapies, it is important to identify differences in care that may lead to disparate outcomes. This review provides a critical appraisal of the empirical research on racial and ethnic disparities in incidence, survival, and outcomes in patients with hematologic malignancies. The review focuses on patients with acute myeloid leukemia, acute lymphocytic leukemia, multiple myeloma, non-Hodgkin lymphoma, Hodgkin lymphoma, myeloproliferative neoplasms, and myelodysplastic syndrome. The review discusses possible causes of racial and ethnic disparities and also considers future directions for studies to help decrease disparities.

The Influence of Treatment Facility Volume on Survival of Non-Hodgkin Lymphoma: Determining the Volume-Outcome Relationship

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 266-266
Author(s):  
Ronald S. Go ◽  
Mohammed Al-Hamadani ◽  
Cynthia S Crowson ◽  
Nilay D Shah ◽  
Elizabeth B Habermann
Keyword(s):  
Hodgkin Lymphoma ◽  
Hospital Volume ◽  
Smoothing Splines ◽  
Cancer Data ◽  
Non Hodgkin Lymphoma ◽  
Outcome Relationship ◽  
The Us ◽  
Facility Type ◽  
Non Linear ◽  
Volume Outcome

Abstract Background: Non-Hodgkin lymphoma (NHL) is a relatively uncommon cancer with annual incidence of ~70,000 cases but with over 50 distinct subtypes. The goal of this study was to determine the extent to which the number of NHL patients treated annually in a facility (facility volume) affects overall survival (OS). This study used the National Cancer Data Base (NCDB), a nationwide oncology database covering 70% of the US cancer population, to address this question. Methods: We used the NCDB to identify patients with NHL diagnosed from 1998 to 2006. Year 2006 was used as a cut-off in order to allow a minimum of five years of follow-up for all patients. Only patients treated at facilities with continuous annual reporting to NCDB were included. We classified treatment facilities by quartiles based on facility volume (mean patients/year): Quartile 1 (Q1: 2-13), Quartile 2 (Q2: 14-20), Quartile 3 (Q3: 21-32) and Quartile 4 (Q4: ≥33). We used Pearson correlation methods to examine collinearity, unadjusted Kaplan-Meier methods to estimate OS rates, log rank test to compare survival distributions, and multivariable Cox proportional hazards model to examine the associations between hospital volume and OS adjusting for other covariates of interest. We also included random effects for hospital to more fully adjust for clustering of outcomes within hospitals. To examine non-linear effects of hospital volume, we utilized smoothing splines. Results: There were 278,985 NHL patients cared for at 1,151 facilities. The distribution of patients according to facility volume was Q1 (10.7%), Q2 (13.5%), Q3 (23.3%) and Q4 (52.5%) and according to facility type was academic (31.2%), comprehensive community (55.9%), community (10.6%) and other (2.3%) centers. The unadjusted median OS by facility volume was: Q1: 61.8 months, Q2: 65.9 months, Q3: 71.4 months and Q4: 83.6 months. After multivariable analysis adjusting for demographic (sex, age, race, ethnicity), socioeconomic (income, insurance type), geographic (area of residence), disease-specific (NHL subtype, stage) and facility-specific (type and location) factors, we show that facility volume remains an independent predictor of all-cause mortality. Compared to patients treated at Q4 facilities, patients treated at lower quartile facilities had a worse OS (Q3HR: 1.05 [95% CI, 1.04-1.06]; Q2HR: 1.08 [1.07-1.10]; Q1HR: 1.14 [1.11-1.17]). We adjusted for hospital as a random effect, performed sensitivity analyses removing primary payor and facility type (due to collinearity with age and facility volume, respectively), and adjusted for Charlson-Deyo co-morbidity score (available only for patients diagnosed after 2003) in secondary models and found similar results. Using smoothing splines, we found a significant non-linear effect of hospital volume on OS (P <0.001). This is depicted in the Figure wherein the hazard ratio of 1.0 corresponded to the average predicted hazard, which occurred at a hospital volume of 59 patients per year. Conclusions: Patients who were treated for NHL at higher volume facilities had longer OS than those who were treated at facilities with a lower volume. This is the first study in the US using a national sample to show that a volume-outcome relationship exists in the medical management of cancer. Figure 1. Figure 1. Disclosures No relevant conflicts of interest to declare.

Second Malignancies Among Elderly Multiple Myeloma Patients Exposed to Bortezomib and Other Treatments: An Analysis of the US SEER-Medicare Linked Database,

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 3972-3972
Author(s):  
Dina Gifkins ◽  
Megan McAuliffe ◽  
Amy Matcho ◽  
Jane Porter ◽  
Scott Chavers ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Hodgkin Lymphoma ◽  
Solid Tumors ◽  
Myeloid Leukemia ◽  
Lymphocytic Leukemia ◽  
Second Malignancy ◽  
Second Malignancies ◽  
Non Hodgkin Lymphoma ◽  
Equity Ownership ◽  
Medicare Database

Abstract Abstract 3972 Second hematologic malignancies have been found to occur at a higher rate among multiple myeloma patients compared to the general population. Although alkylating therapy has been suggested to play a role, the underlying causes remain largely unclear. Increased survival benefit has been documented with the introduction of novel agents over the past decade, and, as noted in other cancers, there may also be a higher occurrence of second malignancies in the era of novel therapies. Recently, data from Phase III studies suggest that patients treated with lenalidomide with prior exposure to melphalan may have an increased risk compared to placebo. However, the contribution of other specific agents has not been well characterized. Evaluation of second malignancies in clinical trial and product safety data for bortezomib has not revealed an increased incidence in bortezomib-treated patients. Additionally, our follow-up study of the VISTA clinical trial participants after 5 years showed no elevation in risk (San Miguel, et al. ASH 2011). To expand our current knowledge, we are conducting a population-based study using the NCI SEER-Medicare database (NCI SEER cancer registry linked with diagnostic and treatment claims data of Medicare beneficiaries) to evaluate bortezomib and other standard treatment exposures in relation to second malignancies subsequent to multiple myeloma. Using the NCI SEER-Medicare database, we identified all multiple myeloma patients with their first diagnosis between 1 Jan 2000 and 31 Dec 2007 aged 66 years or older. Exposure to chemotherapy was identified via Medicare claims, and second malignancies, defined as invasive cancers whose onset was after bortezomib-based therapy and occurring at least 2 months after the initial multiple myeloma diagnosis, were identified from the SEER registries. We identified the number of second malignancies among elderly patients with multiple myeloma and following bortezomib exposure; expanded multivariate analyses, adjusted for exposures, will be presented at the meeting. A total of 9,377 multiple myeloma patients were identified (median age 76 years; 50% males). During the study period, 2,285 (21%) patients had any documented exposure to bortezomib (with or without other treatments). Patients with bortezomib exposure had a median age of 73 years, and 55% were male. Among these 2,285 patients with bortezomib exposure, 33 patients (1.4%) developed a second malignancy (4 [0.2%] hematologic and 29 [1.3%] solid tumors) during the study period after their first documented bortezomib exposure. Hematologic tumors were non-Hodgkin lymphoma (n=3) and acute myeloid leukemia (n=1). Solid tumors were prostate (n=4), bladder (n=4), lung and bronchus (n=3), colon (excluding rectum) (n=3), breast (n=3), and other (n=12). Among the 7,092 multiple myeloma patients with no documented exposure to bortezomib, 320 (4.5%) developed a second malignancy (55 [0.8%] hematologic and 265 [3.7%] solid tumors) during the study period. Hematologic tumors were non-Hodgkin lymphoma (n=16), acute myeloid leukemia (n=7), chronic lymphocytic leukemia (n=2), acute lymphocytic leukemia (n=1), chronic myeloid leukemia (n=1), Hodgkin lymphoma (n=1), and other (n=27). Solid tumors were lung and bronchus (n=46), prostate (n=38), colon (excluding rectum) (n=33), melanoma (n=23), bladder (n=21), breast (n=17), and other (n=87). Based on more than 9,000 elderly multiple myeloma patients, we found a lower prevalence of second malignancies among persons exposed to bortezomib compared to those with no documented bortezomib exposure in our unadjusted analysis. To account for survival and adjust for other exposures, expanded analyses will be presented at the meeting, including standardized incidence ratios and calculations of absolute excess risk among patients exposed to bortezomib and other standard treatments compared to the general SEER population, cumulative incidence of second malignancy for each treatment group adjusting for death as a competing risk, and multivariate analyses to assess risk while adjusting for prior and concomitant treatments and other risk factors. Disclosures: Gifkins: Janssen Research & Development: Employment; Johnson & Johnson: Equity Ownership. McAuliffe:Millennium Pharmaceuticals, Inc.: Employment. Matcho:Janssen Research & Development: Employment; Johnson & Johnson: Equity Ownership. Porter:Millennium Pharmaceuticals, Inc.: Employment. Chavers:Janssen Research & Development: Employment; Johnson & Johnson: Equity Ownership. Ponsillo:Millennium Pharmaceuticals, Inc.: Employment. King:Janssen Research & Development: Employment; Johnson & Johnson: Equity Ownership. Desai:Janssen Research & Development: Employment; Johnson & Johnson: Equity Ownership. Cakana:Janssen Research & Development: Employment; Johnson & Johnson: Equity Ownership. Esseltine:Millennium Pharmaceuticals, Inc.: Employment; Johnson & Johnson: Equity Ownership.

scholarly journals Autoimmune Complications in Hematologic Neoplasms

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1532
Author(s):  
Wilma Barcellini ◽  
Juri Alessandro Giannotta ◽  
Bruno Fattizzo
Keyword(s):  
Lupus Erythematosus ◽  
Checkpoint Inhibitors ◽  
Myeloproliferative Neoplasms ◽  
Pure Red Cell Aplasia ◽  
Lymphocytic Leukemia ◽  
Cell Transplant ◽  
Acute Leukemias ◽  
Hematopoietic Stem ◽  
Non Hodgkin Lymphoma ◽  
Diagnostic Difficulties

Autoimmune cytopenias (AICy) and autoimmune diseases (AID) can complicate both lymphoid and myeloid neoplasms, and often represent a diagnostic and therapeutic challenge. While autoimmune hemolytic anemia (AIHA) and immune thrombocytopenia (ITP) are well known, other rarer AICy (autoimmune neutropenia, aplastic anemia, and pure red cell aplasia) and AID (systemic lupus erythematosus, rheumatoid arthritis, vasculitis, thyroiditis, and others) are poorly recognized. This review analyses the available literature of the last 30 years regarding the occurrence of AICy/AID in different onco-hematologic conditions. The latter include chronic lymphocytic leukemia (CLL), lymphomas, multiple myeloma, myelodysplastic syndromes (MDS), chronic myelomonocytic leukemia (CMML), myeloproliferative neoplasms, and acute leukemias. On the whole, AICy are observed in up to 10% of CLL and with higher frequencies in certain subtypes of non-Hodgkin lymphoma, whilst they occur in less than 1% of low-risk MDS and CMML. AID are described in up to 30% of myeloid and lymphoid patients, including immune-mediated hemostatic disorders (acquired hemophilia, thrombotic thrombocytopenic purpura, and anti-phospholipid syndrome) that may be severe and fatal. Additionally, AICy/AID are found in about 10% of patients receiving hematopoietic stem cell transplant or treatment with new checkpoint inhibitors. Besides the diagnostic difficulties, these AICy/AID may complicate the clinical management of already immunocompromised patients.

scholarly journals Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2419
Author(s):  
Georg Steinbuss ◽  
Mark Kriegsmann ◽  
Christiane Zgorzelski ◽  
Alexander Brobeil ◽  
Benjamin Goeppert ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hodgkin Lymphoma ◽  
Expert Knowledge ◽  
B Cell Lymphoma ◽  
High Accuracy ◽  
Lymphocytic Leukemia ◽  
Non Hodgkin Lymphoma ◽  
Image Patches ◽  
Histopathological Images

The diagnosis and the subtyping of non-Hodgkin lymphoma (NHL) are challenging and require expert knowledge, great experience, thorough morphological analysis, and often additional expensive immunohistological and molecular methods. As these requirements are not always available, supplemental methods supporting morphological-based decision making and potentially entity subtyping are required. Deep learning methods have been shown to classify histopathological images with high accuracy, but data on NHL subtyping are limited. After annotation of histopathological whole-slide images and image patch extraction, we trained and optimized an EfficientNet convolutional neuronal network algorithm on 84,139 image patches from 629 patients and evaluated its potential to classify tumor-free reference lymph nodes, nodal small lymphocytic lymphoma/chronic lymphocytic leukemia, and nodal diffuse large B-cell lymphoma. The optimized algorithm achieved an accuracy of 95.56% on an independent test set including 16,960 image patches from 125 patients after the application of quality controls. Automatic classification of NHL is possible with high accuracy using deep learning on histopathological images and routine diagnostic applications should be pursued.

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