In the treatment of Non-Hodgkin lymphoma (NHL), multiple therapeutic options are available. Improving outcome predictions are essential to optimize treatment. The metabolic active tumor volume (MATV) has shown to be a prognostic factor in NHL. It is usually retrieved using semi-automated thresholding methods based on standardized uptake values (SUV), calculated from 18F-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) images. However, there is currently no consensus method for NHL. The aim of this study was to review literature on different segmentation methods used, and to evaluate selected methods by using an in house created software tool. A software tool, MUltiple SUV Threshold (MUST)-segmenter was developed where tumor locations are identified by placing seed-points on the PET images, followed by subsequent region growing. Based on a literature review, 9 SUV thresholding methods were selected and MATVs were extracted. The MUST-segmenter was utilized in a cohort of 68 patients with NHL. Differences in MATVs were assessed with paired t-tests, and correlations and distributions figures. High variability and significant differences between the MATVs based on different segmentation methods (p < 0.05) were observed in the NHL patients. Median MATVs ranged from 35 to 211 cc. No consensus for determining MATV is available based on the literature. Using the MUST-segmenter with 9 selected SUV thresholding methods, we demonstrated a large and significant variation in MATVs. Identifying the most optimal segmentation method for patients with NHL is essential to further improve predictions of toxicity, response, and treatment outcomes, which can be facilitated by the MUST-segmenter.

OBJECTIVE: The characteristics of COVID-19 in haematologic patients compared to non-haematologic patients have seldom been analyzed. Our aim was to analyze whether there are differences in clinical characteristics and outcome of haematologic patients with COVID-19 as compared to non-haematologic.
METHODS: Retrospective cohort study in 2 University hospitals of patients admitted with laboratory-confirmed COVID-19 included in the SEMICOVID19 database. The cohort with underlying haematologic disease was compared to a cohort of age and date-of-COVID-19-matched controls without haematologic disease (1:2).
RESULTS: 71 cases and 142 controls were included from March-May 2020.Twenty (28.1%) had received recent chemotherapy. Twelve (16.9%) were stem cell transplant recipients (SCT). Eleven (15.5%) were neutropenic concurrently with COVID-19 diagnosis.Haematologic patients presented ARDS (58.5 vs 20.7%, p = 0.0001), thrombotic complications (15.7 vs 2.1%, p = 0.002), DIC (5.7 vs 0.0%, p = 0.011), heart failure (14.3 vs 4.9%, p = 0.029) and required ICU admission (15.5 vs 2.8%, p = 0.001), MV (14.1% vs 2.1%, p 0.001), steroid (64.8 vs 33.1%, p = 0.0001), tocilizumab (33.8 vs 8.5%, p = 0.0001) or anakinra treatment (9.9% vs 0%, p = 0.0001) more often. In-hospital mortality was significantly higher (38.0% vs 18.3%, p = 0.002).
CONCLUSIONS: Our results suggest COVID-19 has worse outcomes in haematologic patients than in non-haematologic, independently of age, and that the development of ARDS and thrombotic complications drive the higher in-hospital mortality.

BACKGROUND: Long-term survivors of Hodgkin lymphoma (HL) and mediastinal non-Hodgkin lymphoma experience late adverse effects of radiotherapy and/or anthracycline-containing chemotherapy, leading to premature cardiovascular morbidity and mortality.
OBJECTIVE: The aim of this study was to identify markers for subclinical cardiovascular disease using cardiovascular magnetic resonance (CMR) in survivors of HL and non-Hodgkin lymphoma.
METHODS: CMR was performed in 80 lymphoma survivors treated with mediastinal radiotherapy with or without anthracyclines, and results were compared with those among 40 healthy control subjects matched for age and sex.
RESULTS: Of the 80 lymphoma survivors, 98% had histories of HL, the mean age was 47 ± 11 years, and 54% were male. Median radiotherapy dose was 36 Gy (interquartile range: 36-40 Gy), and radiotherapy was combined with anthracyclines in 70 lymphoma survivors (88%). Mean time between diagnosis and CMR was 20 ± 8 years. Significantly lower left ventricular (LV) ejection fraction (53% ± 5% vs 60% ± 5%; P < 0.001) and LV mass (47 ± 10 g/m2 vs 56 ± 8 g/m2; P < 0.001) and higher LV end-systolic volume (37 ± 8 mL/m2 vs 33 ± 7 mL/m2; P = 0.013) were found in lymphoma survivors. LV global strain parameters were also significantly worse in lymphoma survivors (P < 0.02 for all). Native myocardial T1 was significantly higher in lymphoma survivors compared with healthy control subjects (980 ± 33 ms vs 964 ± 25 ms; P = 0.007), and late gadolinium enhancement was present in 11% of the survivors.
CONCLUSIONS: Long-term lymphoma survivors have detectable changes in LV function and native myocardial T1 on CMR. Further longitudinal studies are needed to assess the implication of these changes in relation to treatment and clinical outcome.

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UNASSIGNED: To distinguish between sepsis only vs progressive lymphoma in patients with a history of lymphoma who present to the hospital with lactic acidosis.
UNASSIGNED: We identified patients with non-Hodgkin lymphoma (NHL) or Hodgkin lymphoma from January 2014 to December 2015. Patients were categorized into 2 groups: sepsis only or progressive lymphoma. Two-sided Wilcoxon rank sum test and χ1/Fisher exact test were used to compare the continuous and categorical variables, respectively. Kaplan-Meier analysis was used to estimate overall survival (OS).
UNASSIGNED: A total of 51 patients were identified; 33 (65%) patients were categorized into the sepsis only group, and 18 (35%), into the progressive lymphoma group. Values for serum lactate dehydrogenase (LDH) drawn during hospitalization were statistically different between the sepsis only and progressive lymphoma groups (median, 262 vs 665 U/L; P=.005), respectively. The sensitivity and specificity of serum LDH level 2 or more times the upper limit of normal for progressive lymphoma were 56% (95% CI, 33% to 79%) and 85% (95% CI, 73% to 97%), respectively. Serum LDH level was independently predictive of inferior OS (hazard ratio, 27.8; 95% CI, 4.0 to 160.1; P<.001), while serum albumin level (hazard ratio, 0.05; 95% CI, 0.01 to 0.27; P<.001) was independently predictive of improved OS.
UNASSIGNED: Serum LDH levels used in conjunction with serial serum lactate values may be reliable markers to differentiate patients with progressive lymphomatous disease from patients with lymphoma with sepsis only. The LDH levels should be obtained in all patients with lymphoma who present to the hospital with lactic acidosis.

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OBJECTIVE: To assess our adherence to treatment guidelines for diffuse large B-cell lymphoma (DLBCL) established by the American Society of Hematology in 2014 through implementation of a quality improvement initiative (QII) at our institution in 2015.
METHODS: Patients with newly diagnosed DLBCL treated from January 1, 2006, through December 31, 2017, were identified. Electronic medical records were reviewed for documentation of American Society of Hematology Practice Improvement Module quality measures (eg, key pathologic features of DLBCL, lymphoma staging, and screening for hepatitis B virus [HBV] infection in patients receiving rituximab-based chemotherapy). We also reviewed assessment of prognosis by revised International Prognostic Index score, testing for hepatitis C virus, HBV, and HIV, chemotherapy education, and the addition of rituximab in the treatment regimen of CD20+ DLBCL.
RESULTS: Following QII implementation, we saw improvements in most metrics, including reporting of key molecular features (fluorescence in situ hybridization for c-MYC, BCL2, and BCL6, from 45.5% [75 of 165 patients] before QII to 91.7% [22 of 24 patients] after QII; P<.001), screening for HBV (41.8% [69 of 165 patients] to 91.7% [22 of 24 patients]; P<.001) and HIV infections (33.9% [56 of 165 patients] to 87.5% [21 of 24 patients]; P<.0001), providing chemotherapy education (92.7% [153 of 165 patients] to 100%), and use of rituximab for CD20+ DLBCL (83.6% [138 of 165 patients] to 100%; P=.05). All patients had positron emission tomography-computed tomography for DLBCL staging, and there was significantly lower use of bone marrow biopsy (P=.011).
CONCLUSIONS: Implementating a QII and employing standardized metrics can aid in improving quality of care for patients with newly diagnosed DLBCL and allow opportunities to build and ensure better adherence to evolving patient care guidelines.

Next Generation Sequencing (NGS) has dramatically improved the flexibility and outcomes of cancer research and clinical trials, providing highly sensitive and accurate high-throughput platforms for large-scale genomic testing. In contrast to whole-genome (WGS) or whole-exome sequencing (WES), targeted genomic sequencing (TS) focuses on a panel of genes or targets known to have strong associations with pathogenesis of disease and/or clinical relevance, offering greater sequencing depth with reduced costs and data burden. This allows targeted sequencing to identify low frequency variants in targeted regions with high confidence, thus suitable for profiling low-quality and fragmented clinical DNA samples. As a result, TS has been widely used in clinical research and trials for patient stratification and the development of targeted therapeutics. However, its transition to routine clinical use has been slow. Many technical and analytical obstacles still remain and need to be discussed and addressed before large-scale and cross-centre implementation. Gold-standard and state-of-the-art procedures and pipelines are urgently needed to accelerate this transition. In this review we first present how TS is conducted in cancer research, including various target enrichment platforms, the construction of target panels, and selected research and clinical studies utilising TS to profile clinical samples. We then present a generalised analytical workflow for TS data discussing important parameters and filters in detail, aiming to provide the best practices of TS usage and analyses.

The aims of this study were to provide life expectancy (LE) estimates of cancer patients at diagnosis and LE changes over time since diagnosis to describe the impact of cancer during patients\' entire lives. Cancer patients\' LE was calculated by standard period life table methodology using the relative survival of Italian patients diagnosed in population-based cancer registries in 1985-2011 with follow-up to 2013. Data were smoothed using a polynomial model and years of life lost (YLL) were calculated as the difference between patients\' LE and that of the age- and sex-matched general population. The YLL at diagnosis was highest at the youngest age at diagnosis, steadily decreasing thereafter. For patients diagnosed at age 45 years, the YLL was above 20 for lung and ovarian cancers and below 6 for thyroid cancer in women and melanoma in men. LE progressively increased in patients surviving the first years, decreasing thereafter, to approach that of the general population. YLL in the long run mainly depends on attained age. Providing quantitative data is essential to better define clinical follow-up and plan health care resource allocation. These results help assess when the excess risk of death from tumour becomes negligible in cancer survivors.