scholarly journals The Transition between Telomerase and ALT Mechanisms in Hodgkin Lymphoma and Its Predictive Value in Clinical Outcomes

Cancers ◽  
2018 ◽  
Vol 10 (6) ◽  
pp. 169 ◽  
Author(s):  
Radhia M’kacher ◽  
Corina Cuceu ◽  
Mustafa Al Jawhari ◽  
Luc Morat ◽  
Monika Frenzel ◽  
...  
Keyword(s):  
Hodgkin Lymphoma ◽  
Clinical Outcomes ◽  
Predictive Value

FDG-PET in Hodgkin lymphoma

2008 ◽  
Vol 47 (06) ◽  
pp. 235-238 ◽  
Author(s):  
M. Dietlein ◽  
C. Mauz-Körholz ◽  
A. Engert ◽  
P. Borchmann ◽  
O. Sabri ◽  
...  
Keyword(s):  
Hodgkin Lymphoma ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Fdg Pet ◽  
Study Groups ◽  
Therapy Control ◽  
German Study ◽  
High Negative Predictive Value ◽  
Consolidation Radiotherapy

SummaryThe high negative predictive value of FDG-PET in therapy control of Hodgkin lymphoma is proven by the data acquired up to now. Thus, the analysis of the HD15 trial has shown that consolidation radiotherapy might be omitted in PET negative patients after effective chemotherapy. Further response adapted therapy guided by PET seems to be a promising approach in reducing the toxicity for patients undergoing chemotherapy. The criteria used for the PET interpretation have been standardized by the German study groups for Hodgkin lymphoma patients and will be reevaluated in the current studies.

scholarly journals Serum alpha-fetoprotein and clinical outcomes in patients with advanced hepatocellular carcinoma treated with ramucirumab

2021 ◽  
Author(s):  
Andrew X. Zhu ◽  
Richard S. Finn ◽  
Yoon-Koo Kang ◽  
Chia-Jui Yen ◽  
Peter R. Galle ◽  
...  
Keyword(s):  
Prognostic Factor ◽  
Clinical Outcomes ◽  
Predictive Value ◽  
Survival Benefit ◽  
Radiographic Progression ◽  
Cox Regression ◽  
Selection Criterion ◽  
Alpha Fetoprotein ◽  
Advanced Hepatocellular Carcinoma ◽  
Clinical Trial Registration

Abstract Background Post hoc analyses assessed the prognostic and predictive value of baseline alpha-fetoprotein (AFP), as well as clinical outcomes by AFP response or progression, during treatment in two placebo-controlled trials (REACH, REACH-2). Methods Serum AFP was measured at baseline and every three cycles. The prognostic and predictive value of baseline AFP was assessed by Cox regression models and Subpopulation Treatment Effect Pattern Plot method. Associations between AFP (≥ 20% increase) and radiographic progression and efficacy were assessed. Results Baseline AFP was confirmed as a continuous (REACH, REACH-2; p < 0.0001) and dichotomous (≥400 vs. <400 ng/ml; REACH, p < 0.01) prognostic factor, and was predictive for ramucirumab survival benefit in REACH (p = 0.0042 continuous; p < 0.0001 dichotomous). Time to AFP (hazard ratio [HR] 0.513; p < 0.0001) and radiographic (HR 0.549; p < 0.0001) progression favoured ramucirumab. Association between AFP and radiographic progression was shown for up to 6 (odds ratio [OR] 5.1; p < 0.0001) and 6–12 weeks (OR 1.8; p = 0.0065). AFP response was higher with ramucirumab vs. placebo (p < 0.0001). Survival was longer in patients with an AFP response than patients without (13.6 vs. 5.6 months, HR 0.451; 95% confidence interval, 0.354–0.574; p < 0.0001). Conclusions AFP is an important prognostic factor and a predictive biomarker for ramucirumab survival benefit. AFP ≥ 400 ng/ml is an appropriate selection criterion for ramucirumab. Clinical Trial Registration ClinicalTrials.gov, REACH (NCT01140347) and REACH-2 (NCT02435433).

scholarly journals FDG PET/CT to detect bone marrow involvement in the initial staging of patients with aggressive non-Hodgkin lymphoma: results from the prospective, multicenter PETAL and OPTIMAL>60 trials

2021 ◽  
Author(s):  
Dominic Kaddu-Mulindwa ◽  
Bettina Altmann ◽  
Gerhard Held ◽  
Stephanie Angel ◽  
Stephan Stilgenbauer ◽  
...  
Keyword(s):  
Bone Marrow ◽  
B Cell ◽  
Hodgkin Lymphoma ◽  
Predictive Value ◽  
Fdg Pet ◽  
Bone Marrow Involvement ◽  
Non Hodgkin Lymphoma ◽  
Pet Ct ◽  
Fdg Pet Ct ◽  
Initial Staging

Abstract Purpose Fluorine-18 fluorodeoxyglucose positron emission tomography combined with computed tomography (FDG PET/CT) is the standard for staging aggressive non-Hodgkin lymphoma (NHL). Limited data from prospective studies is available to determine whether initial staging by FDG PET/CT provides treatment-relevant information of bone marrow (BM) involvement (BMI) and thus could spare BM biopsy (BMB). Methods Patients from PETAL (NCT00554164) and OPTIMAL>60 (NCT01478542) with aggressive B-cell NHL initially staged by FDG PET/CT and BMB were included in this pooled analysis. The reference standard to confirm BMI included a positive BMB and/or FDG PET/CT confirmed by targeted biopsy, complementary imaging (CT or magnetic resonance imaging), or concurrent disappearance of focal FDG-avid BM lesions with other lymphoma manifestations during immunochemotherapy. Results Among 930 patients, BMI was detected by BMB in 85 (prevalence 9%) and by FDG PET/CT in 185 (20%) cases, for a total of 221 cases (24%). All 185 PET-positive cases were true positive, and 709 of 745 PET-negative cases were true negative. For BMB and FDG PET/CT, sensitivity was 38% (95% confidence interval [CI]: 32–45%) and 84% (CI: 78–88%), specificity 100% (CI: 99–100%) and 100% (CI: 99–100%), positive predictive value 100% (CI: 96–100%) and 100% (CI: 98–100%), and negative predictive value 84% (CI: 81–86%) and 95% (CI: 93–97%), respectively. In all of the 36 PET-negative cases with confirmed BMI patients had other adverse factors according to IPI that precluded a change of standard treatment. Thus, the BMB would not have influenced the patient management. Conclusion In patients with aggressive B-cell NHL, routine BMB provides no critical staging information compared to FDG PET/CT and could therefore be omitted. Trial registration NCT00554164 and NCT01478542

scholarly journals Development and validation of a prognostic COVID-19 severity assessment (COSA) score and machine learning models for patient triage at a tertiary hospital

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Verena Schöning ◽  
Evangelia Liakoni ◽  
Christine Baumgartner ◽  
Aristomenis K. Exadaktylos ◽  
Wolf E. Hautz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Clinical Outcomes ◽  
Predictive Value ◽  
Validation Cohort ◽  
Tertiary Hospital ◽  
Learning Models ◽  
Clinical Risk ◽  
Machine Learning Models ◽  
Risk Stratification Score

Abstract Background Clinical risk scores and machine learning models based on routine laboratory values could assist in automated early identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients at risk for severe clinical outcomes. They can guide patient triage, inform allocation of health care resources, and contribute to the improvement of clinical outcomes. Methods In- and out-patients tested positive for SARS-CoV-2 at the Insel Hospital Group Bern, Switzerland, between February 1st and August 31st (‘first wave’, n = 198) and September 1st through November 16th 2020 (‘second wave’, n = 459) were used as training and prospective validation cohort, respectively. A clinical risk stratification score and machine learning (ML) models were developed using demographic data, medical history, and laboratory values taken up to 3 days before, or 1 day after, positive testing to predict severe outcomes of hospitalization (a composite endpoint of admission to intensive care, or death from any cause). Test accuracy was assessed using the area under the receiver operating characteristic curve (AUROC). Results Sex, C-reactive protein, sodium, hemoglobin, glomerular filtration rate, glucose, and leucocytes around the time of first positive testing (− 3 to + 1 days) were the most predictive parameters. AUROC of the risk stratification score on training data (AUROC = 0.94, positive predictive value (PPV) = 0.97, negative predictive value (NPV) = 0.80) were comparable to the prospective validation cohort (AUROC = 0.85, PPV = 0.91, NPV = 0.81). The most successful ML algorithm with respect to AUROC was support vector machines (median = 0.96, interquartile range = 0.85–0.99, PPV = 0.90, NPV = 0.58). Conclusion With a small set of easily obtainable parameters, both the clinical risk stratification score and the ML models were predictive for severe outcomes at our tertiary hospital center, and performed well in prospective validation.

scholarly journals Pathogenetic and predictive value of biomarkers in patients with ALI and lower severity of illness: results from two clinical trials

2012 ◽  
Vol 303 (8) ◽  
pp. L634-L639 ◽  
Author(s):  
Ashish Agrawal ◽  
Hanjing Zhuo ◽  
Sandra Brady ◽  
Joseph Levitt ◽  
Jay Steingrub ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Clinical Outcomes ◽  
Predictive Value ◽  
Secondary Analysis ◽  
Activated Protein C ◽  
Oxygenation Index ◽  
Severity Of Illness ◽  
Plasminogen Activator Inhibitor ◽  
High Plasma ◽  
Plasminogen Activator Inhibitor 1

Plasma and bronchoalveolar lavage (BAL) biomarkers related to the pathogenesis of acute lung injury (ALI) have previously been associated with poorer clinical outcomes and increased disease severity among patients with ALI. Whether these biomarkers have predictive value in a less severely ill population that excludes septic patients with high APACHE II scores is currently unknown. We tested the association of plasma and BAL biomarkers with physiological markers of ALI severity or clinically relevant outcomes in a secondary analysis of a clinical trial of activated protein C for the treatment of ALI. Plasma plasminogen activator inhibitor-1 (PAI-1) and mini-BAL protein were both significantly associated with increased oxygenation index ( P = 0.02 and 0.01, respectively), whereas there was a trend toward an association between IL-6 and oxygenation index ( P = 0.057). High plasma IL-6, thrombomodulin, and mini-BAL protein were all significantly associated with fewer ventilator-free days (VFDs) ( P = 0.01, 0.01, and 0.05, respectively); no markers were associated with mortality, but we hypothesized that this was due to the small size of our cohort and the low death rate. To confirm these associations in a larger sample, we identified a restricted cohort of patients from the ARDS Network ALVEOLI study with similar baseline characteristics. We retested the associations of the significant biomarkers with markers of severity and clinical outcomes and studied IL-8 as an additional biomarker given its important predictive value in prior studies. In this restricted cohort, IL-6 was significantly associated with oxygenation index ( P = 0.02). Both IL-6 and IL-8 were associated with decreased VFDs and increased 28-day mortality. Future studies should be focused on examining larger numbers of patients with less severe ALI to further test the relative predictive value of plasma and mini-BAL biomarkers for clinically relevant outcomes, including VFDs and mortality, and for their prospective utility in risk stratification for future clinical trials.

scholarly journals PREDICTIVE VALUE OF QUANTITATIVE 18 F‐FDG‐PET‐CT RADIOMICS ANALYSIS IN 174 PATIENTS WITH RELAPSED/REFRACTORY CLASSICAL HODGKIN LYMPHOMA

2021 ◽  
Vol 39 (S2) ◽  
Author(s):  
J Driessen ◽  
G. J. C Zwezerijnen ◽  
H Schöder ◽  
A. J Moskowitz ◽  
M. J Kersten ◽  
...  
Keyword(s):  
Hodgkin Lymphoma ◽  
Predictive Value ◽  
Fdg Pet ◽  
Classical Hodgkin Lymphoma ◽  
Pet Ct ◽  
Fdg Pet Ct

scholarly journals Which Clinical Scoring Systems Are Most Useful in Showing Severity in COVID-19 Patients?

2021 ◽  
Author(s):  
Ishak San ◽  
Emin Gemcioglu ◽  
Salih Baser ◽  
Nuray Yilmaz Cakmak ◽  
Abdulsamet Erden ◽  
...  
Keyword(s):  
High Risk ◽  
Clinical Outcomes ◽  
Predictive Value ◽  
Independent Predictor ◽  
Severe Disease ◽  
Scoring Systems ◽  
High Risk Group ◽  
World Health ◽  
Severe Group ◽  
Clinical Scoring

Abstract IntroductionIn this study, we compare the predictive value of clinical scoring systems that are already in use in patients with COVID-19, including the BCRSS, qSOFA, SOFA, MuLBSTA and HScore, for determining the severity of the disease. Our aim in this study is to determine which scoring system is most useful in determining disease severity and to guide clinicians.Materials and MethodsWe classified the patients into two groups according to the stage of the disease (severe and non-severe) by using the slightly modified and adopted interim guidance of the World Health Organization. Severe cases were divided into a group of surviving patients and a deceased group according to the prognosis. According to admission values, the BCRSS, qSOFA, SOFA, MuLBSTA, and HScore were evaluated at admission using the worst parameters available in the first 24 hours.ResultsOf the 417 patients included in our study, 46 (11%) were in the severe group, while 371 (89%) were in the non-severe group. Of these 417 patients, 230 (55.2%) were men. The median (IQR) age of all patients was 44 (25) years. In multivariate logistic regression analyses, BRCSS in the highest tertile (HR: 6.1, 95% CI: 2.105–17.674, p = 0.001) was determined as an independent predictor of severe disease in cases of COVID-19. In multivariate analyses, qSOFA was also found to be an independent predictor of severe COVID-19 (HR: 4.757, 95% CI: 1.438–15.730, p = 0.011). The area under the curve (AUC) of the BRCSS, qSOFA, SOFA, MuLBSTA, and HScore was 0.977, 0.961, 0.958, 0.860, and 0.698, respectively.ConclusionCalculation of the BRCSS and qSOFA at the time of hospital admission can predict critical clinical outcomes in patients with COVID-19, and their predictive value is superior to that of HScore, MuLBSTA, and SOFA. With early identification of the high-risk group using BRCSS and qSOFA, early interventions for high-risk patients can improve clinical outcomes in COVID-19.

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