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QUANTITATIVE METABOLIC PARAMETERS EVALUATION IN PATIENTS WITH AGGRESSIVE B-CELL LYMPHOMAS TREATED WITH ANTI-CD19 CAR T-CELLS

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Hematological Oncology 2021年第S2期39卷

作者： A. Guidetti A. Dodero G. Argiroffi P. Verderio A. Lorenzoni A. Chiappella C. Carniti E. Seregni s. Pizzamiglio A. Alessi P. Corradini Fondazione IRCCS Istituto Nazionale dei Tumori Hematology Milano Italy Fondazione IRCCS Istituto dei Tumori Nuclear Medicine Milano Italy Fondazione IRCCS Istituto Nazionale dei Tumori Unit of Bioinformatics and Biostatistics Milano Italy

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Back to the Colorectal Cancer Consensus Molecular Subtype Future

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Current Gastroenterology Reports 2019年第2期21卷 1-12页

作者： Menter, David G. Davis, Jennifer S. Broom, Bradley M. Overman, Michael J. Morris, Jeffrey Kopetz, Scott Department of Gastrointestinal Medical Oncology The University of Texas MD Anderson Cancer Center 1515 Holcombe Boulevard--Unit 0426 Houston 77030 TX United States Department of Biostatistics The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston 77030 TX United States

Purpose of Review: This review seeks to provide an informed prospective on the advances in molecular profiling and analysis of colorectal cancer (CRC). The goal is to provide a historical context and current summary on how advances in gene and protein sequencing technology along with computer capabilities led to our current bioinformatic advances in the field. Recent Findings: An explosion of knowledge has occurred regarding genetic, epigenetic, and biochemical alterations associated with the evolution of colorectal cancer. This has led to the realization that CRC is a heterogeneous disease with molecular alterations often dictating natural history, response to treatment, and outcome. The consensus molecular subtypes (CMS) classification classifies CRC into four molecular subtypes with distinct biological characteristics, which may form the basis for clinical stratification and subtype-based targeted intervention. Summary: This review summarizes new developments of a field moving “Back to the Future.” CRC molecular subtyping will better identify key subtype specific therapeutic targets and responses to therapy. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

关键词： CMS Colorectal cancer Consensus molecular subtypes RNAseq

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1837P Assessing PSA

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Annals of Oncology 2023年 34卷 S994-S995页

作者： S. Halabi A. Roy S. Guo L. Rydzewska P.J. Godolphin M.K. Parmar M. Hussain C. Tangen I.M. Thompson M.A. Carducci M.R. Smith M.J. Morris G. Gravis D. Dearnaley P. Verhagen T. Goto N.D. James M.E. Buyse J. Tierney C. Sweeney Biostatistics and Bioinformatics Duke University Medical Center Durham NC USA Biostatistics and Bioinformatics Duke University School of Medicine Durham NC USA Meta-Analysis Unit UCL - University College London London UK MRC Clinical Trials Unit at UCL MRC - Medical Research Council Clinical Trials Unit - University College London (UCL) London UK Institute for Clinical Trials and Methodology MRC - Medical Research Council Clinical Trials Unit - University College London (UCL) London London UK Feinberg School of Medicine Northwestern University Feinberg School of Medicine Chicago IL USA Biostatistics Fred Hutchinson Cancer Research Center Seattle WA USA Urology Christus Health San Antonio San Antonio TX USA Oncology Department Johns Hopkins University Baltimore MD USA Cancer Center Massachusetts General Hospital Cancer Center and Harvard Medical School Boston MD USA Medicine Department MSKCC - Memorial Sloan Kettering Cancer Center New York NY USA Medical Oncology Department Immunity and Cancer Team IPC - Institut Paoli-Calmettes Marseille France ICR ICR - Institute of Cancer Research London UK Urology Erasmus MC Rotterdam Netherlands Urology Kyoto University Kyoto Japan Radiotherapy and Imaging Institute of Cancer Research and Royal Marsden Hospital London UK International Drug Development Institute International Drug Development Institute Louvain-la-Neuve Ottignies Belgium MRC Clinical Trials Unit at UCL UCL - University College London London UK South Australian Immunogenomics Cancer Institute The University of Adelaide Adelaide SA Australia

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Comparing conventional statistical models and machine learning in a small cohort of South African cardiac patients

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Informatics in Medicine Unlocked 2022年 34卷

作者： Premsagar, Preesha Aldous, Colleen Esterhuizen, Tonya M. Gomes, Byron J. Gaskell, Jason William Tabb, David L. Department of Internal Medicine Nelson R Mandela School of Medicine University of Kwa-Zulu Natal South Africa Nelson R Mandela School of Medicine University of Kwa-Zulu Natal South Africa Division of Epidemiology/Biostatistics Department of Global Health Faculty of Medical and Health Sciences Stellenbosch University South Africa Financial Mathematics and BSc Actuarial Science and Statistics University of Witwatersrand South Africa B.Bus.Sci Actuarial Science Specialising in Actuarial Science University of Cape Town South Africa Institut Pasteur Université Paris Cité CNRS UAR 2024 Mass Spectrometry for Biology Unit 28 rue du Dr Roux Cedex 15 PARIS 75724 France Centre for Bioinformatics and Computational Biology Stellenbosch University Stellenbosch South Africa

Background: Machine learning is used to process big data volumes with complex non-linear relationships between predictive variables and predictions. Research into the usefulness of machine learning in small data volumes remains limited. Aim: To compare conventional statistical methods and machine learning to predict angiogram outcomes in a small cohort of South African cardiac patients. Methods: This is a retrospective study on patients with cardiac risk factors at Inkosi Albert Luthuli Central Hospital, Durban, South Africa, from 2002 to 2008. Models were designed using predictive risk factors to forecast a binary angiogram outcome (normal or abnormal) by applying conventional statistical models (binary logistic and log binomial) and stacking ensemble machine learning. Results: The outcome prevalence of abnormal angiograms was 99/173 (57%). Predictive data was used to model this outcome. The binary logistic regression model, which estimates odds ratio, was unsuitable. The log binomial model, which estimates relative risk, did not converge after various stepwise modelling attempts. Thereafter, machine learning models were used. These included logistic regression, k-nearest neighbour, decision tree, support vector machine, and naïve Bayes. The ensemble model amalgamated all algorithms and showed accuracy >70% and excellent performance at different thresholds with an area under the curve (AUC) > 80%. Discussion: The logistic regression model was unsuitable because an odds ratio would have been unreliable and overestimated the true effect since the outcome prevalence was >10%. A log binomial model with relative risk estimates did not converge, possibly owing to the multiple predictive variables. Overall, conventional statistical models were unsuccessful in this instance. Machine learning models had limitations from a small dataset. However, the combined modelling with the stacking ensemble method produced good results in the small, homogenous database by exploiting the

关键词： Angiography Ensemble Machine learning Prediction Statistics

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Machine Learning for Monoclonal Gammopathies of Renal Significance Risk Stratification Using Clinical and Pathology Data

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Kidney International Reports 2025年

作者： Cazzaniga, Giorgio Capitoli, Giulia Barretta, Raffaella Smith, Andrew Vischini, Gisella Papalia, Giuliana Alberici, Federico Mescia, Federica Eccher, Albino Becker, Jan Ulrich Naesens, Maarten Furian, Lucrezia Schröppel, Bernd Galimberti, Stefania Pagni, Fabio Department of Medicine and Surgery Pathology University of Milano-Bicocca IRCCS Fondazione San Gerardo dei Tintori Monza Italy Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center School of Medicine and Surgery University of Milan - Bicocca Monza Italy Biostatistics and Clinical Epidemiology Fondazione IRCCS San Gerardo dei Tintori Monza Italy Department of Medicine and Surgery Proteomics and Metabolomics Unit University of Milano-Bicocca Monza Italy Nephrology Dialysis and Renal Transplant Unit IRCCS Azienda Ospedaliero-Universitaria di Bologna Alma Mater Studiorum University of Bologna Italy Department of Medical and Surgical Science Alma Mater Studiorum - University of Bologna Bologna Italy Department of Pharmacy Health and Nutritional Sciences University of Calabria Rende Italy Nephrology Unit Uniersity of Brescia ASST Spedali Civili di Brescia Brescia Italy Department of Medical and Surgical Sciences for Children and Adults University of Modena and Reggio Emilia University Hospital of Modena Modena Italy Institute of Pathology University Hospital of Cologne Cologne Germany Department of Microbiology Immunology and Transplantation Nephrology and Kidney Transplantation Research Group KU Leuven Leuven Belgium Department of Nephrology and Kidney Transplantation University Hospitals Leuven Leuven Belgium Kidney and Pancreas Transplantation Unit Department of Surgical Oncological and Gastroenterological Sciences School of Medicine and Surgery University of Padua Padua Italy Section of Nephrology University Hospital Ulm Germany

Introduction: The prompt detection of monoclonal gammopathies of renal significance (MGRS) is clinically relevant for the initiation of chemotherapy. Although clinical and laboratory data can suggest the presence of MGRS, renal biopsy still represents the gold standard, despite not always being performed or eventually postponed because it is not deemed useful or informative. In this retrospective study, machine learning (ML) is used to build a tool to assist the prebiopsy risk stratification of MGRS and reinforce the rationale for the histological examination. Methods: The study included a total of 258 patients with monoclonal gammopathy of undetermined significance, of which 168 MGRS cases (65%) and 90 non-MGRS cases (35%) based on the final renal biopsy result. Results: Patients with MGRS were more frequently female (48.2% vs. 28.9%, P = 0.004) and had more frequent Bence Jones proteinuria (73.2% vs. 41.1%, P < 0.001) than non-MGRS cases, with amyloidosis being the most common diagnosis (62%). The ML model achieved an accuracy of 0.79 (95% confidence interval [CI]: 0.67–0.88) and an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.65–0.93) in the validation set. The model is available as a free desktop and Android mobile app (MGRS Interactive Resource for AI-Guided Evaluation, MIRAGE). Conclusion: The ML-based MGRS risk stratification tool can help in the selection of patients with higher probability to have MGRS on renal biopsy, aiming to direct those patients to a complete histological characterization of the disease. © 2025 International Society of Nephrology

关键词： computational pathology digital health digital pathology MGRS onco-nephrology renal biopsy

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Prevalence and Predictors of Inflammation in Pregnant Women: Multi-Country Analysis From BRINDA Project

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Current Developments in Nutrition 2022年 6卷 924-924页

作者： Luo, Hanqi Cole, Chelsea Jahan, Afrin Peerson, Janet Ko, Yi-An Addo, O. Yaw Suchdev, Parminder Young, Melissa Emory University Rollins School of Public Health Emory University Hubert Department of Global Health Hubert Department of Global Health Rollins School of Public Health Emory University University of California Davis Department of Biostatistics and Bioinformatics Rollins School of Public Health Emory University Centers for Disease Control and Prevention Nutrition Branch International Micronutrient Malnutrition Prevention and Control Program Unit Nutrition Branch International Micronutrient Malnutrition Prevention and Control Program Unit Centers for Disease Control and Prevention

Objectives:Limited data exist on the prevalence and predictors of inflammation during pregnancy. We aimed to characterize the inflammatory pattern and predictors of subclinical inflammation across pregnancy using multi-country analysis. Methods:The Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project compiled 17 datasets of pregnant women (n = 14,077) from 15 countries in both high- and low-income settings. Datasets were included if at least one inflammation biomarker (C-reactive protein, CRP, or α-1-acid glycoprotein, AGP) were collected. We estimated the prevalence of any subclinical inflammation (defined as CRP >5 mg/L or AGP >1 g/L), examined AGP and CRP patterns throughout pregnancy, and assessed the relationship between inflammation and covariates such as maternal age, gestational age, socioeconomic, and water and sanitation factors for each dataset. Results:The prevalence of inflammation varied from 16.6% in Afghanistan to < 1% in Vietnam using elevated AGP and from 52.9% in the US to 7.6% in Vietnam using elevated CRP. Inflammation was common but varied across datasets: >40% in 5 datasets, 20–40% in 6 datasets, 10–< 20% in 5 datasets and < 10% in one dataset. AGP decreased with increasing gestational age (P < 0.01 in all seven datasets with gestational age information); however, the magnitude of decrease in AGP varied by country. In contrast, CRP showed an inconsistent pattern by gestational age. In multivariable models, the predictors of inflammation included age, trimester, urban or rural residence, socioeconomic status, improved sanitation, improved drinking water, lactating and smoking status, although strengths of association differed by dataset. Conclusions:Although there was considerable heterogeneity in the prevalence of inflammation, inflammation was common across pregnancy in diverse settings. AGP, a measure of long-term inflammation, decreased across pregnancy in all countries, whereas the pattern for CRP was

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CO5.2 - Méthodes d'analyses transcriptomiques: fiabilité des rangs des gènes sélectionnés et performance diagnostique. Etude de simulation et application QUID-NASH

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Revue d'Épidémiologie et de Santé Publique 2023年 71卷

作者： P. Arnault de Guenyveau J. Lascano Maillard C. Laouénan F. Mehl F. Letourneur D. Valla J. Mullaert E. Gloaguen AP-HP - Hôpital Bichat DEBRC - Unité de recherche clinique Paris Nord Val de Seine Paris France Vital-IT Group SIB Swiss Institute for Bioinformatics Bioinformatics Lausanne Switzerland Université de Paris UMR1137 (IAME) UMR1137 (IAME) Inserm Paris France GenomIC Université de Paris Institut Cochin Inserm CNRS GenomIC Paris France Université Paris Cité Inserm Centre de recherche sur l'inflammation Paris France AP-HP Hôpital Beaujon Service d'hépatologie Clichy-la-Garenne France Institut Curie Inserm U900 Biostatistics for Personalized Medicine Saint-Cloud France

Introduction Les deux principaux objectifs des études transcriptomiques sont: l'identification des gènes différentiellement exprimés (DEG) entre deux groupes et le développement de mod...

Introduction Les deux principaux objectifs des études transcriptomiques sont: l'identification des gènes différentiellement exprimés (DEG) entre deux groupes et le développement de modèles prédictifs. Identifier les DEG dans le bon ordre peut être pertinent pour orienter les analyses fonctionnelles et pourrait permettre, en outre, de développer des modèles prédictifs plus performants. Plusieurs méthodes statistiques pour l'analyse de données transcriptomiques existent, conduisant à des résultats souvent différents. Nous proposons ici d’évaluer la fiabilité des classements des gènes sélectionnés et les performances prédictives obtenues par cinq méthodes largement utilisées: « Linear model for microarray data » (limma) suivi de la méthode de correction de Benjamini-Hochberg, « Significance Analysis of Microarrays » (SAM), « Least Absolute Schrinkage and Selection Operator » (LASSO, validation croisée classique et validation croisée imbriquée), « Sparse Partial Least Square Discriminant Analysis » (SPLSDA) et « Random Forest » (RF). Méthodes Pour les comparer, nous avons simulé des données pour lesquelles les rangs réels des gènes étaient connus. Neuf scénarii ont été envisagés avec différentes forces d'association, niveaux de bruit, tailles d'échantillon et pourcentages de DEG. Nous avons utilisé le Tau de Kendall pondéré pour mesurer la fiabilité des rangs estimés des DEG, et l'aire sous la courbe ROC (AUC) pour leur performance diagnostique. S'en est suivie une application aux données réelles de l'étude QUID-NASH (« Quantitative Imaging in Diabetes - Non-Alcoholic SteatoHepatitis »). Résultats En situation de faible niveau de bruit, les méthodes limma et SAM sont significativement plus performantes que les méthodes multivariées. Dans les cas de bruit élevé, SAM et LASSO (version validation croisée imbriquée) ont fourni des classements plus fiables. Comme attendu, la fiabilité des classements se détériore avec l'augmentation du niveau de bruit ou la diminution de la

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Braix Risk Score: An Automated Mammogram-Based Biomarker for Breast Cancer Created by Applying Artificial Intelligence

SSRN

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SSRN 2024年

作者： Hopper, John L. Nguyen, Tuong Linh Elliott, Michael S. Al-Qershi, Osamah Schmidt, Daniel F. Makalic, Enes Li, Shuai Fox, Samantha K. Dowty, James G. Peña-Solorzano, Carlos Andres Kwok, Chun Fung Chen, Yuanhong Wang, Chong Lippey, Jocelyn F. Brotchie, Peter Carneiro, Gustavo McCarthy, Davis J. Frazer, Helen M.L. Centre for Epidemiology & Biostatistics Melbourne School of Population and Global Health University of Melbourne Victoria Australia Bioinformatics and Cellular Genomics Unit St Vincent’s Institute of Medical Research Victoria Australia Melbourne Integrative Genomics School of Mathematics and Statistics School of BioSciences Faculty of Science University of Melbourne Victoria Australia Faculty of Information Technology Monash University ClaytonVIC Australia Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care University of Cambridge Cambridge United Kingdom Precision Medicine School of Clinical Sciences at Monash Health Monash University ClaytonVIC Australia Murdoch Children’s Research Institute Royal Children’s Hospital ParkvilleVIC Australia School of Computer Science Australian Institute for Machine Learning University of Adelaide SA Australia Department of Surgery St Vincent’s Hospital Melbourne VIC Australia Faculty of Medicine Dentistry and Health Sciences University of Melbourne Victoria Australia St Vincent’s BreastScreen St Vincent’s Hospital Melbourne VIC Australia Department of Radiology St Vincent’s Hospital Melbourne VIC Australia Centre for Vision Speech and Signal Processing University of Surrey United Kingdom BreastScreen Victoria VIC Australia

Background: Artificial intelligence (AI) algorithms to detect breast cancers from mammographic images are being developed. We evaluated the performance of an algorithm across all stages of the screening process and beyond. Methods: We used an image-specific AI-based breast cancer detection measure learnt from images of women attending BreastScreen Victoria (BRAIx cancer detection measure) to create a woman-specific standard normal mammography-based risk score (BRAIx risk score). Applying logistic regression to a random independent prospective testing cohort of 96,348 women, we estimated risk of invasive breast cancers: (i) detected at cohort entry (525);and (ii) for women given an all-clear, diagnosed during the next 4 years either at future screens (790) or during intervals between screens (308), as a function of the BRAIx risk score and other measures. We estimated the odds ratio per standard deviation (OR) and a greater risk in an upper ***: The BRAIx risk score predicted reader outcomes, recall, cancer detection and future risk. For 4-years risk, OR=2.70 (95% confidence interval (CI) 2.52 to 2.88) overall with evidence for a greater OR in the upper tail (P=10-18). The predicted absolute risk was >10% for 1% of all-clear women. BRAIx risk score was greater for women with a family history and explained 23% (95% CI 21-25%) of familial aspects of risk. Mammographic density was weakly associated with risk before (OR=1.27;95% CI 1.20 to 1.35), but not after, fitting ***: The BRAIx risk score is a strong biomarker for breast cancer that could be used to inform risk-based recall for further assessment, detection and future screening. Its use would automatically identify all-clear women at similar risk to BRCA1 and BRCA2 mutation carriers. BRAIx could also reveal insights into both familial and non-familial causes. Funding: Medical Research Future Fund, the Ramaciotti Foundation, National Breast Cancer Foundation, Cancer *** o

关键词： Mammography

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Correction: High arterial oxygen levels and supplemental oxygen administration in traumatic brain injury: insights from CENTER-TBI and OzENTER-TBI

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Intensive care medicine 2023年第2期49卷 269-272页

作者： Emanuele Rezoagli Matteo Petrosino Paola Rebora David K Menon Stefania Mondello D James Cooper Andrew I R Maas Eveline J A Wiegers Stefania Galimberti Giuseppe Citerio School of Medicine and Surgery University of Milano - Bicocca Monza Italy. Department of Emergency and Intensive Care San Gerardo University Hospital Extracorporeal Membrane Oxygenation (ECMO) Center Fondazione IRCCS San Gerardo dei Tintori Monza Italy. Department of Medicine and Surgery Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center University of Milano - Bicocca Monza Italy. Division of Anaesthesia Addenbrooke's Hospital University of Cambridge Hills Road Box 93 Cambridge CB2 0QQ UK. Department of Biomedical and Dental Sciences and Morphofunctional Imaging University of Messina Messina Italy. Intensive Care Department Alfred Hospital Melbourne Australia. School of Public Health and Preventive Medicine Monash University Melbourne Australia. Antwerp University Hospital and University of Antwerp Edegem Belgium. Department of Public Health Erasmus MC University Medical Center Rotterdam The Netherlands. School of Medicine and Surgery University of Milano - Bicocca Monza Italy. giuseppe.citerio@unimib.it. NeuroIntensive Care Unit Neuroscience Department Fondazione IRCCS San Gerardo dei Tintori Monza Italy. giuseppe.citerio@unimib.it.

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Anemia With and Without Iron Deficiency During Pregnancy and Association With Adverse Birth Outcomes: BRINDA Project

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Current Developments in Nutrition 2025年 9卷

作者： Lei Liu Hanqi Luo Rochelle E Werner Maria Elena D Jefferds Yaw Addo Parminder S Suchdev Ramakrishnan Usha Phuong Hong Nguyen Kathryn G Dewey Maria J Ramírez-Luzuriaga Melissa F Young Yi-An Ko Emory University United States Emory University Rollins School of Public Health Hubert Department of Global Health Centers for Disease Control and Prevention Nutrition Branch United States CDC Central America Office Division of Global Health Protection United States International Food Policy Research Institute (IFPRI) Nutrition Diets and Health Unit United States Institute for Global Nutrition and Department of Nutrition University of California Davis United States Diabetes Epidemiology and Clinical Research Section National Institute of Diabetes and Digestive and Kidney Diseases National Institutes of Health United States Emory University Rollins School of Public Health Department of Biostatistics and Bioinformatics United States

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