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Current practices of follow-up programs for childhood cancer survivors in Italy

Tumori 2023年第6期109卷 555-561页

作者： Terenziani, Monica Tozzi, Alberto Eugenio Diaco, Laura Biasin, Eleonora Cattoni, Alessandro Croci, Ileana Fraschini, Donatella Giorgiani, Giovanna Haupt, Riccardo Muraca, Monica Pillon, Marta Sironi, Giovanna Valsecchi, Maria Grazia Mastronuzzi, Angela Pediatric Unit Fondazione IRCCS Istituto Nazionale dei Tumori Lombardia Milan Italy Predictive and Preventive Medicine Research Unit Bambino Gesù Children's Hospital IRCCS Lazio Rome Italy FIAGOP - Federazione Italiana Associazioni Genitori e Guariti Oncoematologia Pediatrica Rome Italy Pediatric Onco-Hematology Regina Margherita Children Hospital Piemonte Turin Italy Department of Pediatrics Università degli Studi di Milano-Bicocca Fondazione MBBM Ospedale San Gerardo Lombardia Monza Italy Pediatric Onco-Hematology Fondazione IRCCS Policlinico San Matteo Lombardia Pavia Italy DOPO Clinic Division of Pediatric Hematology and Oncology IRCCS Istituto Giannina Gaslini Liguria Genoa Italy Clinic of Pediatric Hemato-Oncology Department of Women's and Children's Health University of Padova Veneto Padova Italy Bicocca Centre of Bioinformatics Biostatistics and Bioimaging School of Medicine and Surgery University of Milano Bicocca Lombardia Milan Italy Department of Onco-Hematology Gene Therapy Cell Therapies and Hemopoietic Transplant Bambino Gesù Children's Hospital IRCCS Lazio Rome Italy

Introduction: Quality of life in childhood cancer survivors is largely affected by survivorship care and transition from treatment to long-term follow-up (LTFU). Referring to evidence-based recommendations, we wanted to evaluate LTFU care for survivors through a survey among the Italian Association for Pediatric Hematology-Oncology (AIEOP) centers. The project aimed to evaluate the availability of services in Italy, investigate strengths and weaknesses, analyze improvements of awareness in the field, and identify the gaps that need to be addressed by different centers. Methods: Together with the family representatives, on behalf of AIEOP’s Late Effects Working Group, we developed a questionnaire on assisting childhood cancer survivors. All AIEOP centers received one questionnaire including information on local health system organizations;LTFU for childhood cancer survivors;services for adult survivors of childhood cancer;information provided to survivors/caregivers and care plan delivery. Results: Forty-eight AIEOP centers were contacted and 42 replied, with a response rate of 87.5%. The majority of respondents confirmed their interest in assisting patients with a survivorship care plan (95.2%), regardless of a clinic or dedicated staff. Discussion: This is the first overview of LTFU in Italy, which provides detailed results at national levels, prompting consideration of improvements in the last decade. Although there is a high level of interest in survivorship care, many centers lack resources to implement such programs. The identification of these challenges is useful for planning future strategies. © Fondazione IRCCS Istituto Nazionale dei Tumori 2023.

关键词： childhood cancer survivors long-term follow-up Survey

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521P Deciphering a three-miRNA signature as a prognostic biomarker in glioma patients: Correlation with DFS and OS

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Annals of Oncology 2023年 34卷 S399-S400页

作者： A.B. Diaz Mendez M. Di Giuliani A. Sacconi G. Regazzo S. Ramponi G. Blandino C. Garufi M.G. Rizzo Translational Oncology Research Unit IRCCS Istituto Nazionale Tumori Regina Elena Rome Italy Biostatistics and Bioinformatics Unit IRCCS Istituto Nazionale Tumori Regina Elena Rome Italy Medical-Oncology Unit Azienda Ospedaliera San Camillo Forlanini Rome Italy

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Prediction of Driver Gene Matching in Lung Cancer NOG/PDX Models Based on Artificial Intelligence

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Engineering 2022年第8期8卷 102-114页

作者： Yayi He Haoyue Guo Li Diao Yu Chen Junjie Zhu Hiran C.Fernando Diego Gonzalez Rivas Hui Qi Chunlei Dai Xuzhen Tang Jun Zhu Jiawei Dai Kan He Dan Chan Yang Yang School of Medicin5 Tongji UniversityShanghai 200092China Department of Medical Oncology Shanghai Pulmonary HospitalSchool of Medicin5Tongji UniversityShanghai 200433China Department of Automation School of Electronic Information and Electrical Engineerin7Shanghai Jiao Tong UniversityShanghai 200240China Spine Center Orthopedic DepartmentShanghai Changzheng HospitalShanghai 200003China Department of Thoracic Surgery Shanghai Pulmonary HospitalSchool of Medicin5Tongji UniversityShanghai 200433China School of Materials Science and Engineerin7 Tongji UniversityShanghai 201804China Department of Thoracic Surgery Allegheny General HospitalPittsburg8PA 15212USA Department of Thoracic Surgery and Minimally Invasive Thoracic Surgery Unit(UCTMI) Coruna University HospitalCoruna 15006Spain Oncology and Immunology BU Research Service DivisionWuXi Appte3Shanghai 200131China SJTU–Yale Joint Center for Biostatistics and Data Scienc5 Department of Bioinformatics and BiostatisticsSchool of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghai 200240China Division of Medical Oncology Department of Medicin5University of Colorado Anschutz Medical CampusAuror1CO 80045USA

Patient-derived tumor xenografts(PDXs)are a powerful tool for drug discovery and screening in ***,current studies have led to little understanding of genotype mismatches in PDXs,leading to massive economic ***,we established PDX models from 53 lung cancer patients with a genotype matching rate of 79.2%(42/53).Furthermore,17 clinicopathological features were examined and input in stepwise logistic regression(LR)models based on the lowest Akaike information criterion(AIC),least absolute shrinkage and selection operator(LASSO)-LR,support vector machine(SVM)recursive feature elimination(SVM-RFE),extreme gradient boosting(XGBoost),gradient boosting and categorical features(Cat Boost),and the synthetic minority oversampling technique(SMOTE).Finally,the performance of all models was evaluated by the accuracy,area under the receiver operating characteristic curve(AUC),and F1 score in 100 testing *** multivariable LR models revealed that age,number of driver gene mutations,epidermal growth factor receptor(EGFR)gene mutations,type of prior chemotherapy,prior tyrosine kinase inhibitor(TKI)therapy,and the source of the sample were powerful ***,Cat Boost(mean accuracy=0.960;mean AUC=0.939;mean F1 score=0.908)and the eight-feature SVM-RFE(mean accuracy=0.950;mean AUC=0.934;mean F1 score=0.903)showed the best performance among the ***,application of the SMOTE improved the predictive capability of most models,except Cat *** on the SMOTE,the ensemble classifier of single models achieved the highest accuracy(mean=0.975),AUC(mean=0.949),and F1 score(mean=0.938).In conclusion,we established an optimal predictive model to screen lung cancer patients for non-obese diabetic(NOD)/Shi-scid,interleukin-2 receptor(IL-2R)γ^(null)(NOG)/PDX models and offer a general approach for building predictive models.

关键词： Machine learning Patient-derived tumor xenografts NOG mice

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Building Clinically Actionable Models for Predicting Mechanical Complications in Postoperatively Well-Aligned Adult Spinal Deformity Patients Using XGBoost Algorithm

SSRN

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SSRN 2022年

作者： Balaban, Baris Yilgor, Caglar Yucekul, Altug Zulemyan, Tais Obeid, Ibrahim Pizones, Javier Kleinstueck, Frank Perez-Grueso, Francisco Javier Sanchez Pellise, Ferran Alanay, Ahmet Sezerman, Osman Ugur Department of Bioinformatics and Biostatistics Acibadem University Istanbul Turkey Department of Orthopedics and Traumatology Acibadem University School of Medicine Istanbul Turkey Acibadem University Turkey Clinique duDos Elsan Jean Villar Private Hospital Bordeaux France Spine Surgery Unit Hospital Universitario La Paz Madrid Spain Spine Center Division Department of Orthopedics and Neurosurgery Schulthess Klinik Switzerland Spine Surgery Unit Hospital Universitari Vall d'Hebron Barcelona Spain

Background: Spinal disorders causing pain in the lower back lead to more global disability than any other medical condition. Surgical correction of spinal deformity can relieve pain and reduce disability, however, more than 45% of operated patients are unfortunately foreseen to suffer mechanical complications. Nowadays, progress in health care comes from data-driven approaches. In an ideal world, clinicians should work closely with data scientists to derive clinical ***: The aim of this collaborative clinical and data engineering study was to build clinically actionable models for predicting mechanical complications in postoperatively well-aligned adult spinal deformity patients using the XGBoost ***: Adult spinal deformity surgery clinical data was collected prospectively in a study group database. Preoperative, operative and postoperative patient characteristics, radiographic measurements, technical details as well as patient-reported outcome measures were retrieved and analyzed. Several feature engineering strategies were applied, so that the splits in the resulting trees can be informative and combined effects of several features can be tested. Variable selection process included correlation analysis. The XGBoost model was built using default parameters where the objective function was "binary logistic", and the booster was "gbtree". The model hyperparameters were optimized with 9-times 3-fold repeated cross-validation in three grid search rounds. An expert-guided model was built upon the initial model to make it clinically sensible, explainable, and actionable, where a methodical expert interference was carried out regardless of the gain scores of the features. The means (and standard deviation) of the area under the receiving operating curve (AUC), sensitivity, specificity, area under the precision-recall curve (AUPRC), precision, recall, and F score was calculated for cross-validation analysis. The importance matrix was built to stratif

关键词： Machine learning

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Incorporating Participants’ Welfare into Sequential Multiple Assignment Randomized Trials

arXiv

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arXiv 2022年

作者： Wang, Xinru Deliu, Nina Narita, Yusuke Chakraborty, Bibhas Centre for Quantitative Medicine Duke-NUS Medical School Singapore MRC - Biostatistics Unit University of Cambridge Cambridge United Kingdom Department of Methods and Models for Economics Territory and Finance Sapienza University of Rome Rome Italy Department of Economics and Cowles Foundation Yale University New HavenCT United States Program in Health Services and Systems Research Duke-NUS Medical School Singapore Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States

Dynamic treatment regimes (DTRs) are sequences of decision rules that recommend treatments based on patients’ time-varying clinical conditions. The sequential multiple assignment randomized trial (SMART) is an experimental design that can provide high-quality evidence for constructing optimal DTRs. In a conventional SMART, participants are randomized to available treatments at multiple stages with balanced randomization probabilities. Despite its relative simplicity of implementation and desirable performance in comparing embedded DTRs, the conventional SMART faces inevitable ethical issues including assigning many participants to the empirically inferior treatment or the treatment they dislike, which might slow down the recruitment procedure and lead to higher attrition rates, ultimately leading to poor internal and external validities of the trial results. In this context, we propose a SMART under the Experiment-as-Market framework (SMART-EXAM), a novel SMART design that holds the potential to improve participants’ welfare by incorporating their preferences and predicted treatment effects into the randomization procedure. We describe the steps of conducting a SMART-EXAM and evaluate its performance compared to the conventional SMART. The results indicate that the SMART-EXAM can improve the welfare of the participants enrolled in the trial, while also achieving a desirable ability to construct an optimal DTR when the experimental parameters are suitably specified. We finally illustrate the practical potential of the SMART-EXAM design using data from a SMART for children with attention-deficit/hyperactivity disorder (ADHD). © 2022, CC BY.

关键词： Design of experiments

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A Bayesian Network to Predict Cardiovascular Diseases in Adolescents and Young Adults with Breast Cancer

SSRN

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SSRN 2024年

作者： Bernasconi, Alice Zanga, Alessio Lucas, Peter J.F. Scutari, Marco Cosimo, Serena Di De Santis, Maria Carmen Rocca, Eliana La Baili, Paolo Cavallo, Ilaria Verderio, Paolo Ciniselli, Chiara M. Pizzamiglio, Sara Blanda, Adriana Perego, Paola Vallerio, Paola Stella, Fabio Trama, Annalisa Evaluative Epidemiology Unit Department of Epidemiology and Data Science Fondazione IRCCS Istituto Nazionale dei Tumori Milano Italy Department of Informatics Systems and Communication University of Milano-Bicocca Milano Italy Data Science and Advanced Analytics F. Hoffmann - La Roche Ltd Basel Switzerland University of Twente Enschede Netherlands Lugano Switzerland Department of Advanced Diagnostics Fondazione IRCCS Istituto Nazionale dei Tumori Milano Italy Radiation Oncology 1 Unit Fondazione IRCCS Istituto Nazionale dei Tumori Milano Italy Department of Radiation Oncology Azienda Ospedaliero Universitaria Integrata Verona Italy Data Science Unit Department of Epidemiology and Data Science Fondazione IRCCS Istituto Nazionale dei Tumori Milano Italy Scientific Direction Fondazione IRCCS Istituto Nazionale dei Tumori Milano Italy Unit of Bioinformatics and Biostatistics Department of Epidemiology and Data Science Fondazione IRCCS Istituto Nazionale dei Tumori Milano Italy Molecular Pharmacology Unit Department of Experimental Oncology Fondazione IRCCS Istituto Nazionale dei Tumori Milano Italy Cardiology Unit Fondazione IRCCS Istituto Nazionale dei Tumori Milano Italy

Background: When dealing with real-world healthcare data, data and knowledge fusion is crucial for providing accurate predictions. In this context, artificial intelligence (AI) tools such as Bayesian Networks (BNs) can be a perfect basis on which to build effective, interpretable models. Recently, the increasing number of adolescent and young adult (AYA) survivors of breast cancer (BC) has highlighted the cardiotoxic impact of cancer therapies in this peculiar and understudied age-group, making cardiovascular diseases (CVDs) among the most frequent long-term sequalae in these young patients. Against this background, we aimed to develop a predictive BN-based model to identify young BC survivors at higher risk of developing CVDs within 5-years from cancer ***: We trained and validated a BN model making use of expert knowledge and data a population-based (1036 patients) and a clinical (339 patient) cohort of female AYA (i.e., aged 18 to 39 years) 1-year survivors of BC, diagnosed in 2009-2014. We validated model performance against standard classification metrics and illustrated a possible model application for ranking patients by their risk probability using a lift ***: The proposed model showed very good classification performance (with all metrics above 69%) and a causal interpretable structure. Additionally, according to the lift chart, focusing on the 25% of 1-year BC survivors at higher risk of developing CVDs based on model predictions, 81% of those who would actually develop a CVD could be ***: In this paper, we developed and validated the first explainable model that can predict CVD risk in AYA survivors of BC. The model’s predictions can be used by clinicians to identify high-risk patients and efficiently rank patients by their predicted risk, to plan personalized follow-up ***: The research leading to these results has received funding from AIRC under IG 2020 - ID. 24864 – P.I. Annalisa ***

关键词： Bayesian networks

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PROMIS-29 in rheumatoid arthritis patients who screen positive or negative for fibromyalgia on MDHAQ FAST4 (fibromyalgia assessment screening tool) or 2011 fibromyalgia criteria

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Seminars in Arthritis and Rheumatism 2024年 66卷 152361-152361页

作者： Gibson, Kathryn A. Kaplan, Robert M. Pincus, Theodore Li, Tengfei Luta, George Department of Rheumatology Liverpool Hospital Ingham Research Institute University of New South Wales Sydney 2170 NSW Australia Clinical Excellence Research Center Department of Medicine Stanford University School of Medicine Stanford 94305 CA United States Division of Rheumatology Department of Internal Medicine Rush University School of Medicine Chicago 60612 Ill United States Department of Biostatistics Bioinformatics & Biomathematics Georgetown University Washington 20057 DC United States Clinical Research Unit The Parker Institute Copenhagen University Hospital Frederiksberg DK-2000 Denmark

Background: PROMIS-29 T-scores query health-related quality of life (HRQL) in 7 domains, physical function, pain, fatigue, anxiety, depression, sleep quality, and social participation, to establish population norms. An MDHAQ (multidimensional health assessment questionnaire) scores these 7 domains and includes medical information such as a FAST4 (fibromyalgia assessment screening tool) index. We analyzed PROMIS-29 T-scores in rheumatoid arthritis (RA) patients vs population norms and for positive vs negative fibromyalgia (FM) screens and compared PROMIS-29 T-scores to MDHAQ scores to assess HRQL. Methods: A cross-sectional study was performed at one routine visit of 213 RA patients, who completed MDHAQ, PROMIS-29, and reference 2011 FM Criteria. PROMIS-29 T-scores were compared in RA vs population norms and in FM+ vs FM- RA patients, based on MDHAQ/FAST4 and reference criteria. Possible associations between PROMIS-29 T-scores and corresponding MDHAQ scores were analyzed using Spearman correlations and multiple regressions. Results: Median PROMIS-29 T-scores indicated clinically and statistically significantly poorer status in 26-29% FM+ vs FM- RA patients, with larger differences than in RA patients vs population norms for 6/7 domains. MDHAQ scores were correlated significantly with each of 7 corresponding PROMIS-29 domains (|rho|≥0.62, p<0.001). Linear regressions explained 55-73% of PROMIS-29 T-score variation by MDHAQ scores and 56%-70% of MDHAQ score variation by PROMIS-29 T-scores. Conclusions: Scores for 7 PROMIS-29 domains and MDHAQ were highly correlated. The MDHAQ is effective to assess HRQL and offers incremental medical information, including FAST4 screening. The results indicate the importance of assessing comorbidities such as fibromyalgia screening in interpreting PROMIS-29 T-scores. © 2024

关键词： clinical measurement FAST4 fibromyalgia MDHAQ patient questionnaires PROMIS-29 rheumatoid arthritis

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Beyond the Brain: MIDS Extends BIDS to Multiple Modalities and Anatomical Regions 32

Beyond the Brain: MIDS Extends BIDS to Multiple Modalities a...

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32nd Medical Informatics Europe Conference, MIE 2022

作者： Saborit-Torres, Jose Manuel Nadal-Almela, Silvia Montell-Serrano, Joaquim Angel Oliver-Garcia, Elena Carceller, Hector Gómez-Ádrian, Jon Ander Caparrós-Redondo, Marisa García-García, Francisco Domenech-Fernández, Julio De La Iglesia-Vayá, Maria Unidad Mixta de Imagen Biomédica FISABIO-CIPF Fund. Para El Fomento de la Invest. Sanitaria y Biomedica de la Comunidad Valenciana-Valencia Spain Cibersam Isc Iii Av. Blasco Ibáñez 15 València46010 Spain Universitat de Valencia Burjassot46100 Spain Department of Computer Systems and Computation Universitat Politècnica de València-València Spain Bioinformatics & Biostatistics Unit Principe Felipe Research Center-Valencia Spain Hospital Arnau de Vilanova-Valencia Spain General Directorate of Research Innovation Technology and Quality Subdirectorate General of Information Systems for Health-Valencia Spain

ISBN: (纸本)9781643682846

Brain Imaging Data Structure (BIDS) provides a valuable tool to organise brain imaging data into a clear and easy standard directory structure. Moreover, BIDS is widely supported by the scientific community and has been established as a powerful standard for medical imaging management. Nonetheless, the original BIDS is restricted to magnetic resonance imaging (MRI) of the brain, limiting its implantation to other techniques and anatomical regions. We developed Medical Imaging Data Structure (MIDS), conceived to extend BIDS methodology to other anatomical regions and multiple imaging systems in these areas. The MIDS standard was developed to store and manage medical images as an extension of BIDS. It allows the user to handily save studies of multiple anatomical regions and imaging techniques. Besides, MIDS improves the classification of multiple images within the structure, allowing the possibility to unify them in a single study to apply on them preprocessing or artificial intelligence algorithms. Finally, the results generated are saved in the derivatives folder. © 2022 European Federation for Medical Informatics (EFMI) and IOS Press.

关键词： Standardization

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Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association 2022年第12期20卷 2929页

作者： D Chamil Codipilly W Scott Harmsen Prasad G Iyer Barrett's Esophagus Unit Division of Gastroenterology and Hepatology Mayo Clinic Rochester Minnesota. Division of Biostatistics and Bioinformatics Mayo Clinic Rochester Minnesota.

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Towards the use of multiple ROIs for radiomics-based survival modelling: finding a strategy of aggregating lesions

arXiv

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arXiv 2024年

作者： Wilk, Agata Malgorzata Swierniak, Andrzej d’Amico, Andrea Suwiński, Rafal Fujarewicz, Krzysztof Borys, Damian Department of Systems Biology and Engineering Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Department of Biostatistics and Bioinformatics Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch Wybrzeze AK 15 Gliwice44-102 Poland Department of Nuclear Medicine and Endocrine Oncology PET Diagnostics Unit Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch Wybrzeze AK 15 Gliwice44-102 Poland II-nd Radiotherapy and Chemotherapy Clinic Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch Wybrzeze AK 15 Gliwice44-102 Poland

Background. Radiomic features, derived from a region of interest (ROI) in medical images, are valuable as prognostic factors. Selecting an appropriate ROI is critical, and many recent studies have focused on leveraging multiple ROIs by segmenting analogous regions across patients –— such as the primary tumour and peritumoral area or subregions of the tumour. These can be straightforwardly incorporated into models as additional features. However, a more complex scenario arises for example in a regionally disseminated disease, when multiple distinct lesions are present. Aim. This study aims to evaluate the feasibility of integrating radiomic data from multiple lesions into survival models. We explore strategies for incorporating these ROIs and hypothesise that including all available lesions can improve model performance. Methods. While each lesion produces a feature vector, the desired result is a unified prediction. We propose methods to aggregate either the feature vectors to form a representative ROI or the modeling results to compute a consolidated risk score. As a proof of concept, we apply these strategies to predict distant metastasis risk in a cohort of 115 non-small cell lung cancer patients, 60% of whom exhibit regionally advanced disease. Two feature sets (radiomics extracted from PET and PET interpolated to CT resolution) are tested across various survival models using a Monte Carlo Cross-Validation framework. Results. Across both feature sets, incorporating all available lesions — rather than limiting analysis to the primary tumour — consistently improved the c-index, irrespective of the survival model used. Conclusion. Lesions beyond the primary tumour carry information that should be utilised in radiomics-based models to enhance predictive ability. © 2024, CC BY-NC-ND.

关键词： Lung cancer

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