Blood Cancer Diagnosis Research Articles

Time Relationship between the Occurrence of a Thromboembolic Event and the Diagnosis of Hematological Malignancies.

Venous and arterial thromboembolism (VTE/ATE) often coexist with onco-hematologic diagnosis. This study aimed to assess the time relationship between the diagnosis of VTE/ATE and blood cancers. The second aim was to identify VTE/ATE risk factors related to the type of hematology disease and cardiac history. A total of 1283 patients underwent cardio-oncology evaluation at the Institute of Hematology and Transfusion Medicine in Warsaw from March 2021 through March 2023 (2 years), and 101 (7.8%) cases were identified with VTE/ATE. ATE compared with VTE significantly occurred more often before the diagnosis and treatment of hematologic malignancy: 33/47 (70.2%) vs. 15/54 (27.8%), p < 0.0001. The risk of a VTE episode is exceptionally high in the first months after the diagnosis of an onco-hematological disease and the initiation of anticancer treatment. The higher frequency of VTE was associated with acute myeloid leukemia (17 cases/270 patients/6.30%/p = 0.055), acute lymphocytic leukemia (7 cases/76 patients/9.21%/p = 0.025), and chronic myeloproliferative disease (7 cases/48 patients/14.58%/p = 0.0003). Only the risk of VTE was significantly increased before (OR = 6.79; 95% CI: 1.85-24.95; p = 0.004) and after diagnosis of myeloproliferative disease (OR = 3.12; 95% CI: 1.06-9.16; p = 0.04). ATEs occur more often than VTE before a diagnosis of blood cancer. The risk of VTE is exceptionally high before and after diagnosis of chronic myeloproliferative disease.

Financial distress and medical financial hardship among young adult survivors of blood cancer.

The long-term financial impact of cancer care has not been adequately addressed in young adults. As part of a remote intervention study, we describe medical financial distress and hardship among young adult survivors of blood cancer at study entry. Young adults were recruited from 6 US hospitals. Using a Research Electronic Data Capture link, young adults confirmed their eligibility-namely, currently 18 to 39 years of age, blood cancer diagnosis 3 or more years ago, off active treatment, and not on parent's insurance. Following consent, the baseline assessment was sent. The primary outcome measure, the Personal Financial Wellness Scale, measured financial distress (scored as severe, 1-2; high, 3-4; average, 5-6; and low to no, 7-10). Medical financial hardship encompassed material hardship, psychological impact, and coping behaviors. Descriptive summary statistics and linear regression were used. Among the 126 participants, 54.5% came from minority racial or ethnic groups. Median time since diagnosis was 10 years (interquartile range = 6-16 years), with 56% having received a diagnosis when they were between 18 and 39 years of age. The overall mean (standard deviation) Personal Financial Wellness Scale score was 5.1 (2.4), but 49% reported severe or high distress. In multivariable analysis, female sex, Hispanic ethnicity, and lower income were strongly associated with worse Personal Financial Wellness Scale scores. Among participants with severe financial distress (n = 26), 72% reported 2 or more household material hardships, had worse scores across all psychological domains, and altered survivorship care because of cost (68%). Nearly half of long-term young adult cancer survivors reported severe or high levels of financial distress. Individuals with severe or high distress also reported more medical financial hardship than other participants. This finding highlights the need for ongoing financial intervention in this vulnerable population. NCT05620979.

Allostatic overload in the medically ill patients: Results from an observational study

The present study aimed at evaluating the prevalence of allostatic overload (AO) among subjects with different medical diseases and explore whether medically ill patients with or without AO differ for specific clinical features (i.e., co-occurring mental or psychosomatic disorders). An observational cross-sectional study was carried out. Outpatients with a diagnosis of blood cancer, systemic sclerosis, or migraine received a clinical assessment which included the Mini International Neuropsychiatric Interview or the Structured Clinical Interview for DSM-5 and the Diagnostic Criteria for Psychosomatic Research-Revised Semi-Structured Interview (DCPR-R SSI). Four hundred and thirty-nine outpatients were enrolled. Among them, 39 (8.9%) had a diagnosis of blood cancer, 200 (45.5%) had a diagnosis of systemic sclerosis, and 200 (45.5%) had a diagnosis of migraine. A total of 104 (23.7%) patients had a DCPR-R diagnosis of AO. Patients with a diagnosis of blood cancer, migraine, or systemic sclerosis did not differ for DCPR-R AO prevalence (P = 0.082). Based on multiple regression analysis, medically ill patients with DCPR-R AO were more likely to satisfy the diagnosis of DCPR-R illness denial (odds ratio [OR] = 2.99, 95% confidence interval [CI] = 1.04 &amp;ndash; 8.58), conversion symptoms (OR = 5.32, 95% CI = 1.16 &amp;ndash; 24.38), or demoralization (OR = 2.57, 95% CI = 1.08 &amp;ndash; 6.11) and a DSM-5 diagnosis of major depressive episode/disorder (OR = 1.90, 95% CI = 1.03 &amp;ndash; 3.50), if compared to those without DCPR-R AO. DCPR-R AO is a clinically useful transdiagnostic feature potentially associated with other psychosomatic syndromes and mental disorders that may contribute to the disease burden and the poor global health conditions of medically ill patients.

Development of a Targeted NGS Panel for Hematologic Malignancies According to Who/ICC 2022 Guidelines

Introduction: Since 2020, we have successfully developed a targeted blood cancer NGS panel and have been using it efficiently in clinical settings. However, it needed to be upgraded due to recent new blood cancer diagnosis and classification guidelines by the World Health Organization and the International Collaboration for Cancer Classification (ICC) in 2022. Therefore, in this study, an updated targeted NGS panel that covers the updated guidelines was evaluated using clinical samples. Methods: To adhere to the recent guidelines, the currently used targeted NGS panel (KBB DNA/RNAseq NGS Leukemia PHB; KBlueBio Inc., Hwasun, Korea) was reviewed. Its analytical performance was validated using standard NA12878 and clinical samples. The clinical samples consisted of 19 genomic DNA and 20 total RNA samples extracted from the bone marrow or peripheral blood of patients with hematologic malignancies, including acute myeloid leukemia, acute lymphocytic leukemia, myelodysplastic syndrome/myeloproliferative neoplasm, multiple myeloma, and lymphoma. Results: The updated DNA panel comprises 125 genes including 6 new genes ( BCORL1, BLM, GNB1, PRPF8, SAMD9 and SAMD9L). The updated RNA panel has 116 genes including 31 new genes ( FLT3, CBFA2T3, RPN1, BCL11B, ZNF362, HLF, NUTM1, UBRF, CDX2, ZEB2, CDK6, ZMYM2, AFDN, ELL, NSD1, HOXA9, KDM5A, ZBTB16, GLIS2, PRDM16, NPM1, KAT6A, MNX1, TAF15, MLLT1, P2RY8, HNRNPUL1, ECM1, ENAM, JCHAIN (IGJ) and MDFIC). The accuracy, repeatability, reproducibility, sensitivity, and detection limit of the updated NGS panels were evaluated using standard materials, and the results met the predefined criteria. A comparison of the updated DNA panel with the old panel using 19 clinical samples revealed an overall concordance rate of 100% between the two panels for all mutations (95% confidence interval (CI): 99.72-100.00%). Similarly, the updated RNA panel also met the predefined criteria. The overall concordance rate between the updated RNA panel and the old panel was 99.72% in 20 clinical samples (95% CI: 99.00-99.72%). Conclusions: The old targeted DNA/RNA NGS panel was successfully updated according to the 2022 WHO and ICC guidelines, and can be used to accurately and efficiently detect the genetic variants of blood cancers.

Adult sibling-related experiences while caring for a parent diagnosed with a blood cancer.

An older parent's blood cancer diagnosis impacts the entire family system, including adult siblings, an often overlooked subsystem of the family. Yet, adult siblings are typically involved in their parents' care needs. We explored sibling-related experiences adult child caregivers identify while caring for a parent diagnosed with a blood cancer to capture information useful for caregiving intervention development. Fifteen adult child caregivers with at least 1 sibling participated in an in-depth, semistructured interview. Participants were 87% white and 80% daughters. A majority of caregivers were in midlife (M age = 44), with parents diagnosed between age 56 and 90. A thematic analysis was conducted on transcripts using the constant comparative method. Caregivers described 3 types of sibling-related experiences that centered on (a) caregiving responsibilities (e.g., sharing/not sharing tasks; challenging feelings about lack of involvement); (b) expectations about the caregiver role (e.g., gender, family status, and birth order expectations); and (c) coping together and apart (e.g., receiving information together, enhanced relationships, divergent maladaptive coping). Findings illustrate how a parent's blood cancer diagnosis can enhance the sibling bond and family system as well as contribute to tension, particularly regarding the experiences of not sharing caregiving tasks or having divergent approaches to coping. Findings also provide insight into areas in which supportive interventions or resources are needed (e.g., helping siblings talk about caregiving involvement) to promote healthy family functioning after a blood cancer diagnosis. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A2M-LEUK: attention-augmented algorithm for blood cancer detection in children

Leukemia is a malignancy that affects the blood and bone marrow. Its detection and classification are conventionally done through labor-intensive and specialized methods. The diagnosis of blood cancer in children is a critical task that requires high precision and accuracy. This study proposes a novel approach utilizing attention mechanism-based machine learning in conjunction with image processing techniques for the precise detection and classification of leukemia cells. The proposed attention-augmented algorithm for blood cancer detection in children (A2M-LEUK) is an innovative algorithm that leverages attention mechanisms to improve the detection of blood cancer in children. A2M-LEUK was evaluated on a dataset of blood cell images and achieved remarkable performance metrics: Precision = 99.97%, Recall = 100.00%, F1-score = 99.98%, and Accuracy = 99.98%. These results indicate the high accuracy and sensitivity of the proposed approach in identifying and categorizing leukemia, and its potential to reduce the workload of medical professionals and improve the diagnosis of leukemia. The proposed method provides a promising approach for accurate and efficient detection and classification of leukemia cells, which could potentially improve the diagnosis and treatment of leukemia. Overall, A2M-LEUK improves the diagnosis of leukemia in children and reduces the workload of medical professionals.

Uptake of COVID-19 vaccination in people with blood cancer: Population-level cohort study of 12 million patients in England

BackgroundPeople with blood cancers have increased risk of severe outcomes from COVID-19 and were prioritised for vaccination. MethodsIndividuals in the QResearch database aged 12 years and above on 1st December 2020 were included in the analysis. Kaplan–Meier analysis described time to COVID-19 vaccine uptake in people with blood cancer and other high-risk disorders. Cox regression was used to identify factors associated with vaccine uptake in people with blood cancer. ResultsThe analysis included 12,274,948 individuals, of whom 97,707 had a blood cancer diagnosis. 92% of people with blood cancer received at least one dose of vaccine, compared to 80% of the general population, but there was lower uptake of each subsequent vaccine dose (31% for fourth dose). Vaccine uptake decreased with social deprivation (HR 0.72, 95% CI 0.70, 0.74 for most deprived versus most affluent quintile for first vaccine). Compared with White groups, uptake of all vaccine doses was significantly lower in people of Pakistani and Black ethnicity, and more people in these groups remain unvaccinated. ConclusionsCOVID-19 vaccine uptake declines following second dose and there are ethnic and social disparities in uptake in blood cancer populations. Enhanced communication of benefits of vaccination to these groups is needed.

What causes delays in diagnosing blood cancers? A rapid review of the evidence.

We undertook a rapid review of literature relating to the diagnosis of blood cancers, to find out what factors contribute to delays in diagnosis, including symptom recognition, appraisal and help-seeking behaviours. We used rapid review methodology following Tricco etal. to synthesise current literature from two electronic databases. We searched for studies about symptom appraisal help-seeking for all blood cancers published between 2001 and 2021, written in English. Fifteen studies were included in the review, of which 10 were published in the United Kingdom. We found a number of factors associated with delays in blood cancer diagnosis. These included patient factors such as gender, age and ethnicity, as well as health system factors such as poor communication and seeing a locum clinician in primary care. A narrative synthesis of the evidence produced four types of symptom interpretation by patients: (1) symptoms compatible with normal state of health, (2) event-linked problems, (3) mild or chronic illness and (4) non-specific unwell state. These four interpretations were linked to different help-seeking behaviours. After seeking help, patients often experienced delays due to healthcare professionals' (HCPs') non-serious interpretation of symptoms, misleading blood tests, discontinuity of care and other barriers in the diagnostic pathway. Blood cancers are difficult to diagnose due to non-specific heterogeneous symptoms, and this is reflected in how those symptoms are interpreted by patients and managed by HCPs. It is important to understand how different interpretations affect delays in help-seeking, and what HCPs can do to support timely follow-up for patients.

Blood cancer diagnosis using hyperspectral imaging combined with the forward searching method and machine learning.

Hyperspectral imaging (HSI), a widely used biosensing technique, has been applied to tumor detection. Rapid, accurate, and low-cost detection of blood cancer using hyperspectral technology remains a challenge. We developed a new method to discriminate blood cancer using hyperspectral imaging (HSI) and the forward searching method (FSM). Four commonly used classification models are applied for four types of blood cancer spectra recognition. The support vector machine (SVM) model with the highest recognition accuracy (94.5%) combined with HSI achieves high-precision tumor identification. For higher recognition accuracy and lower hardware barriers, based on the selection probabilities of spectral lines calculated by a multi-objective atomic orbital search method, the FSM is proposed for HSI feature selection. With the proposed method, the wavelength band range of the spectrum is reduced by at least 50%. Compared with the traditional dimensionality reduction methods, the FSM can obtain a higher accuracy rate with lower hardware requirements. These results show that our proposed method can achieve non-invasive rapid screening of blood cancers with lower hardware requirements. Therefore, HSI assisted with FSM and SVM hybrid models can be a powerful and promising tool for blood cancer detection.

Evaluating the role of financial navigation in alleviating financial distress among young adults with a history of blood cancer: A hybrid type 2 randomized effectiveness-implementation design

BackgroundYoung adulthood (YA) is a complex phase of life, marked by key developmental goals, including educational and vocational attainment, housing independence, maintenance of social relationships, and financial stability. A cancer diagnosis during, or prior to, this phase of life can compromise the achievement of these milestones. Studies of adults with cancer have demonstrated that >70% report experiencing financial side-effects, which are associated with increased mortality, diminished health-related quality of life, and forgone medical care. The goal of this project is to evaluate financial distress of YA-aged survivors of blood cancers, and the impact of financial navigation on alleviating this distress. MethodsThis three-arm, multi-site, hybrid type 2 randomized effectiveness-implementation design (EID) study will be conducted through remote consent, remote data capture and telephone-based/virtual financial navigation. Participants will be aged 18–39, and more than three years from their blood cancer diagnosis. In this six-month intervention, the study will compare the primary outcome of financial distress in three arms: (1) usual care (2) participant-initiated, ad hoc navigation, and (3) study-directed proactive navigation. The study will be evaluated via the five-component Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) outcome strategy with a mixed-methods approach through quantitative assessment of participant-reported financial distress using the Personal Financial Wellness Scale™, as the primary outcome measure, and qualitative assessment through interviews. ConclusionThe study will address many unanswered questions regarding financial navigation within the YA survivor population and will inform the most successful strategies to mitigate financial distress in this vulnerable population.

Enabling the Implementation of Next-Generation Sequencing into Clinical Diagnosis: Nemhesys Project

Training for the use and analysis of Next-Generation Sequencing (NGS) is uncommon, as shown by the lack of specialized health professionals in this area. This delays NGS implementation into the healthcare system, with the result that NGS solutions hardly ever translated into real and precise experiences. . NEMHESYS (NGS-Establishment in Multidisciplinary HEalthcare SYStem) is an Erasmus Plus Knowledge Alliance programme with the aim of improving the translation of NGS into clinical practice for hematological diagnosis and prognosis by providing clinical and biomedical staff with suitable training in NGS NEMHESYS was initiated in March 2020 with partners from five Academic Institutions (University of Salamanca, Queen's University Belfast, University of Helsinki, Central European Institute of Technology (CEITEC) at Masaryk University and Charité University Medicine Berlin) and three small/medium enterprises (SME) (Artelnics, Mnemotix and IDimás). The project developed an online Masterclass for participants to complete over a 12-16-week period. The course, via the Moodle Platform hosted at the University of Helsinki, included a pre-module and five modules of which, in order to successfully pass the course, modules A & B were mandatory and at least one of the optional modules C, D or E. Regarding the contents, Module A was an introduction to all clinical and laboratory essentials for NGS in the clinic, whilst Module B was focused on all aspects of the analysis of NGS data. Module C looked at the barriers and risks around the implementation of NGS in health systems, Module D explored the role of artificial intelligence, linked data and knowledge graph, and Module E examined innovative programs and intra-entrepreneurship for NGS in the clinic. Three cohorts of the NGS Masterclass were run (March 2021, October 2021, and February 2022). Registration was high for each of the cohorts (161, 253, and 172 participants, respectively). Around 75% of the participants were from Europe but a significant number were from USA, Brazil, Argentina, Egypt, Australia as well as from Africa and Middle Eastern countries. The background of the participants was reported to be medics, biomedical and clinical scientists, graduate students, and a small proportion of bioinformaticians. As expected, there was a large number who didn't participate further than registration, but overall, at least 180 participants across the three cohorts successfully completed the Masterclass program. Additionally, participants who belonged to one of the five partner academic institutions, and had passed the online NGS training course, were entitled to apply for a learning mobility opportunity. Pre-covid this had been planned to for periods of 2 -12 weeks, but this was modified to enable more participants to be involved when considering covid related travel and other restrictions. Therefore, five Mobility Workshops were planned of one week duration at each of the academic centres, taking place from April to October (2022), and covering fundamental and of interest aspects of the application of NGS methodologies in the clinical practice and research. At the end, twenty-eight (28) participants were involved in the Mobility. The Brno hands-on workshop "Basic Skills in NGS: from PB sample to the Report" covered the clinical and laboratory essentials of NGS. The Salamanca workshop further explored novel technologies and trends in Real World NGS, whilst a focus on Precision Medicine and blood cancer diagnosis was covered by the Belfast workshop. Then, with more innovative methodologies, Charité covered the 4th generation Nanopore Technology, and the Helsinki workshop focused on Single Cell Sequencing Analytics. Overall, the NEMHESYS Masterclass and mobility program achieved the aim of giving online and practical training to a significant number of clinical and biomedical participants around the globe in the use of NGS for hematological disorders. This will enable the delivery of higher quality clinical services. PROJECT REF.: 612639-EPP-1-2019-1-ES-EPPKA2-KA. Disclaimer: The European Commission support for the production of this publication does not constitute an endorsement of the contents that reflects the views only of the authors, and the Commission cannot be held responsible for any use that may be made of the information contained therein. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Z-Score Normalized Features with Maximum Distance Measure Based k-NN Automated Blood Cancer Diagnosis System

Leukemia is a blood-forming cancer disease characterized by the abnormal growth of White Blood Cells. Early detection and treatment of cancer reduce mortality and increase the survival rate. Most of the k-NN approaches used a different combination of statistical and geometrical features of the nucleus and cytoplasm with and without normalization, resulting in an uncertain range of features. Minkowski, Euclidean, City-block, Correlation, and Cosine distance metrics discover the average similarity between features predicts Leukemia with less accuracy. This paper proposes a Z-Score normalized feature set combined with a k-NN maximum distance measure-based automated blood cancer diagnosis system to address this problem. Initially, the features are normalized using the Z-score normalization method, which eliminates the mis-classification outcomes. The Chebyshev distance measure, which finds the maximum similarities between the features when k=1, has the highest accuracy of 97.92%. Furthermore, tuning the parameters by grid-search improves the performance rate by 98.65% .

Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model

Blood cancer has been a growing concern during the last decade and requires early diagnosis to start proper treatment. The diagnosis process is costly and time-consuming involving medical experts and several tests. Thus, an automatic diagnosis system for its accurate prediction is of significant importance. Diagnosis of blood cancer using leukemia microarray gene data and machine learning approach has become an important medical research today. Despite research efforts, desired accuracy and efficiency necessitate further enhancements. This study proposes an approach for blood cancer disease prediction using the supervised machine learning approach. For the current study, the leukemia microarray gene dataset containing 22,283 genes, is used. ADASYN resampling and Chi-squared (Chi2) features selection techniques are used to resolve imbalanced and high-dimensional dataset problems. ADASYN generates artificial data to make the dataset balanced for each target class, and Chi2 selects the best features out of 22,283 to train learning models. For classification, a hybrid logistics vector trees classifier (LVTrees) is proposed which utilizes logistic regression, support vector classifier, and extra tree classifier. Besides extensive experiments on the datasets, performance comparison with the state-of-the-art methods has been made for determining the significance of the proposed approach. LVTrees outperform all other models with ADASYN and Chi2 techniques with a significant 100% accuracy. Further, a statistical significance T-test is also performed to show the efficacy of the proposed approach. Results using k-fold cross-validation prove the supremacy of the proposed model.

Germline ERBB2/HER2 Coding Variants Are Associated with Increased Risk of Myeloproliferative Neoplasms.

Simple SummaryFamilial clustering of myeloproliferative neoplasms (MPN) is well known, with a 5- to 7-fold increased risk of MPN among first-degree relatives of MPN patients. However, the genetic susceptibility of this disease remains poorly understood. Exome sequencing of a familial MPN pedigree followed by a case-control analysis identified germline variants in the HER2/ERBB2 gene that associate with the MPN phenotype. ERBB2/HER2 is a novel susceptibility locus which contributes to cancer risk in combination with additional risk alleles.Familial cases of myeloproliferative neoplasms (MPN) are relatively common, yet few inherited risk factors have been identified. Exome sequencing of a kindred with a familial cancer syndrome characterized by both MPN and melanoma produced a germline variant in the ERBB2/HER2 gene that co-segregates with disease. To further investigate whether germline ERBB2 variants contribute to MPN predisposition, the frequency of ERBB2 variants was analyzed in 1604 cases that underwent evaluation for hematologic malignancy, including 236 cases of MPN. MPN cases had a higher frequency of rare germline ERBB2 coding variants compared to non-MPN hematologic malignancies (8.9% vs. 4.1%, OR 2.4, 95% CI: 1.4 to 4.0, p = 0.0028) as well as cases without a blood cancer diagnosis that served as an internal control (8.9% vs. 2.7%, OR 3.5, 95% CI: 1.4 to 8.3, p = 0.0053). This finding was validated via comparison to an independent control cohort of 1587 cases without selection for hematologic malignancy (8.9% in MPN cases vs. 5.2% in controls, p = 0.040). The most frequent variant identified, ERBB2 c.1960A > G; p.I654V, was present in MPN cases at more than twice its expected frequency. These data indicate that rare germline coding variants in ERBB2 are associated with an increased risk for development of MPN. The ERBB2 gene is a novel susceptibility locus which likely contributes to cancer risk in combination with additional risk alleles.

Pre-Diagnostic and Cancer-Associated Depression and Anxiety Among Patients with Hematologic Malignancies in the United States

Background: Little is known about mental health disorders among patients with hematologic malignancies. As depression and anxiety substantially impair the quality of life of patients with cancer, the Institute of Medicine recommends diagnosis and management of these disorders as an integral part of cancer care (Cancer Care for the Whole Patient, IOM, 2008). Moreover, it has been suggested that non-White patients are less likely to disclose depression and anxiety to physicians due to concerns about stigma, and that physicians also inadequately screen this population (Bell, Annals FM, 2011). We aimed to characterize the prevalence and sociodemographic predictors of pre-diagnostic and cancer-associated (CA) depression and anxiety among patients with hematologic malignancies in the United States. Methods: We conducted a retrospective analysis using the Surveillance Epidemiology and End Results (SEER)-Medicare database. Patients ≥ 67 years old diagnosed with a hematologic malignancy (lymphoma, myeloma, leukemia, or myelodysplastic syndromes) between 2000 and 2015 who died prior to December 31, 2016 were eligible for inclusion. We examined prevalence of pre-diagnostic depression or anxiety, defined as having at least one inpatient or two outpatient Medicare claims for depression or anxiety starting from 24 months to 1 month prior to their blood cancer diagnosis. We also examined prevalence of CA-depression or anxiety, defined as at least one inpatient or two outpatient claims for these conditions between 1 month prior to and 3 months after their blood cancer diagnosis. CA-depression or anxiety were mutually exclusive with pre-diagnostic depression or anxiety respectively. We performed univariable analysis to determine sociodemographic and clinical covariates of CA-depression or CA-anxiety. Next, we fit multivariable logistic regression models to characterize factors independently associated with these two outcomes, adjusting for potential confounders: blood cancer type, age, sex, race, marital status, income, education, comorbidity regardless of univariable significance, and additional variables with p&amp;lt;0.05 in univariable analysis. Results: A total of 64,018 patients were eligible, of which 53.4% had lymphoma, 18.6% myeloma, 18.0% leukemia, and 10.0% myelodysplastic syndromes. Most patients were White (89.6%) and 51.0% were female. Of the entire cohort, 10.6% had pre-diagnostic depression, 4.4% had CA-depression, 7.4% had pre-diagnostic anxiety, and 2.8% had CA-anxiety. Overall, 20.7% of the cohort met our claims-based definition of at least one of these four mental health disorders. In univariable analysis, patients with CA-depression were more likely to have pre-diagnostic anxiety (10.7% vs. 7.3%, p&amp;lt;0.001), have a high comorbidity score (37.5% vs. 31.8%, p&amp;lt;0.001), be female (56.7% vs. 50.6%, p&amp;lt;0.001), and be unmarried (55.3% vs. 52.0%, p=0.001) versus patients without CA-depression. They were also less likely to be non-White (8.8% vs. 10.5%, p=0.005). All associations except marital status remained significant in multivariable analysis (Table 1). In univariable analysis, patients with CA-anxiety were more likely to have pre-diagnostic depression (18.9% vs. 10.4%, p&amp;lt;0.001) and be female (63.2% vs. 50.5%, p&amp;lt;0.001); they were also less likely to be non-White (7.7% vs. 10.5%, p&amp;lt;0.001) compared to patients without CA-anxiety. All associations remained significant in multivariable analysis (Table 2). Conclusions: In this large cohort of patients with blood cancers, more than one in five individuals struggled with depression or anxiety either before their blood cancer diagnosis or as a new mental health syndrome during the three months afterward. These data suggest a critical need for systematic mental health screening and management for this patient population. Moreover, the fact that patients with pre-diagnostic anxiety or depression were at increased risk of developing CA-depression or anxiety respectively emphasizes the importance of additional psychosocial support for patients with pre-existing mental disorders. Finally, our finding that non-White patients were significantly less likely to develop CA-depression or anxiety is provocative, and suggests that either non-White patients with hematologic malignancies have a lower incidence of these disorders or that the mental health concerns of this population are less likely to be routinely captured. Disclosures No relevant conflicts of interest to declare.

"Home wasn't really home anymore": Understanding caregivers' perspectives of the impact of blood cancer caregiving on the family system.

Research on the impact of family cancer caregiving is primarily dyadic in focus. How caregiving affects the larger family system is less understood, yet knowing this is vital to developing supportive resources for caregivers, patients, and their families. To better understand how blood cancer caregiving impacts the family system, we explored the experiences of adult child caregivers of diagnosed parents and parent caregivers of diagnosed children. We conducted semi-structured interviews with 39 midlife parent and adult child caregivers of patients with leukemia or lymphoma. Using a family systems theory lens, we conducted a thematic analysis using the constant comparative method to identify how caregiving impacts the larger family system. Caregivers ranged from age 30 to 64 (M = 43). They described four ways that caregiving impacted themselves and the larger family system: (1) disruption of home life, (2) emotional (dis)connection, (3) juggling competing roles, and (4) developing resiliency and intimacy. Perspectives within each category differed based on their relational role to the patient or in the broader family. Themes identify ways to provide support to both caregiver types. Support care resources could help families navigate gains and losses impacting the family system after a blood cancer diagnosis. Both caregiver types described experiencing (and/or their family experiencing) a loss in relational connection, feeling alone, and members distancing themselves. Both caregiver types also described gains in family functioning, like strengthened bonds and togetherness. Findings validate the need for family-centered support with key areas to address for healthy family functioning.

Blood cancer diagnosis using ensemble learning based on a random subspace method in laser-induced breakdown spectroscopy.

There are two main challenges in the diagnosis of blood cancer. The first is to diagnose cancer from healthy control, and the second is to identify the types of blood cancer. The chemometrics method combined with laser-induced breakdown spectroscopy (LIBS) can be used for cancer detection. However, chemometrics methods were easily influenced by the spectral feature redundancy and noise, resulting in low accuracy rate because of their simple structure. We proposed an approach using LIBS combined with the ensemble learning based on the random subspace method (RSM). The serum samples were dripped onto a boric acid substrate for LIBS spectrum collection. The complete blood cancer sample set include leukemia [acute myeloid leukemia (AML) and chronic myelogenous leukemia (CML)], multiple myeloma (MM), and lymphoma. The results showed that the accuracy rates using k nearest neighbors (kNN) and linear discriminant analysis (LDA) only were 88.14% and 94.45%, respectively, while using RSM with LDA (RSM-LDA), the average accuracy rate was improved from 94.45% to 98.34%. Furthermore, the variable importance of spectral lines (Na, K, Mg, Ca, H, O, N, C-N) were evaluated by the RSM-LDA model, which can improve the recognition ability of blood cancer types. Comparing the RSM-LDA model and only with LDA, the results showed that the average accuracy rate for cancer type identification was improved from 80.4% to 91.0%. These results demonstrate that LIBS combined with the RSM-LDA model can discriminate the blood cancer from the health control, as well as the recognition the types for blood cancers.

Tolerability of Aerosolized Versus Intravenous Pentamidine for Pneumocystis jirovecii Pneumonia Prophylaxis in Immunosuppressed Pediatric, Adolescent, and Young Adult Patients.

Pentamidine is an antifungal that is used alternatively to sulfamethoxazole-trimethoprim for the prophylaxis and treatment of Pneumocystis jirovecii pneumonia (PJP). The primary objective of this study was to assess the tolerability of aerosolized versus intravenous pentamidine for PJP prophylaxis in pediatric, adolescent, and young adult immunosuppressed patients. Secondary objectives included comparing pentamidine formulation reaction to dosing frequency and diagnosis. This retrospective chart review used electronic medical record (EMR) data from patients at a tertiary care pediatric teaching institution from January 1, 2014, to January 1, 2017. Information used from the EMR included pentamidine dosing, ordering, and laboratory values. Inclusion criteria consisted of patients with a cancer diagnosis, hematopoietic stem cell transplant (HSCT) recipients, and renal transplant recipients who received pentamidine for PJP prophylaxis. Ninety-six patients met inclusion criteria, of which 31 received aerosolized pentamidine. Ten of the 96 patients experienced a drug-related reaction to either aerosolized or intravenous pentamidine (p = 0.134). Nine of 10 patients who experienced a reaction received intravenous pentamidine versus 1 patient who received aerosolized pentamidine (p = 0.132). In those patients who reacted to pentamidine there was a higher incidence of reactions after subsequent administration (p = 0.039) and in patients with a blood cancer diagnosis (p = 0.042). Data suggest that patients who receive aerosolized pentamidine may tolerate therapy better compared to intravenous administration. Additional studies involving larger numbers of pediatric, adolescent, and young adult patients are needed for stronger statistically significant clinical differences in tolerability of aerosolized versus intravenous pentamidine.

Automation of Blood Cancer Risk Analysis Using Fuzzy Logic

The statistical estimation of the cancer research center shows that approximate 23 million new cases were recorded in 2010 in all over the world. Blood cancer is a deadly disease and have different categories. Symptom of every category is different, but some initial symptoms are same. Research and statistics show that in the majority of cases, people know about the disease when it became too late. Fuzzy is being used in plentiful field like engineering, data mining, medical, science and Computational computing. This research work proposes a method which can be able to assist the doctor in diagnosis of blood Cancer. Fuzzy introduce varies inference but famous fuzzy inference types are Mamdani and TSK. In this article Mamdani Inference System has been proposed to measure the Blood Cancer Detector (BCD). The proposed method Fuzzy Logic based Blood Cancer Detector (BCD) is taking 10 input parameters and 1 output variable. The input variables are BI (Blood Infection), Bleeding, Pains, SC (skin changes), AD (Abdominal Discomforts), VP (Vision problem), WL (weight loss), BP (Bladder problems), fatigue &amp; TP (throat problems), and output variable is BCD.