Machine learning in oral squamous cell carcinoma: current status, clinical concerns and prospects for future-A systematic review

Background: Oral cancer can show heterogenous patterns of behavior. For proper and effective management of oral cancer, early diagnosis and accurate prediction of prognosis are important. To achieve this, artificial intelligence (AI) or its subfield, machine learning, has been touted for its potential to revolutionize cancer management through improved diagnostic precision and prediction of outcomes. Yet, to date, it has made only few contributions to actual medical practice or patient care. Objectives: This study provides a systematic review of diagnostic and prognostic application of machine learning in oral squamous cell carcinoma (OSCC) and also highlights some of the limitations and concerns of clinicians towards the implementation of machine learning-based models for daily clinical practice. Data sources: We searched OvidMedline, PubMed, Scopus, Web of Science, and Institute of Electrical and Electronics Engineers (IEEE) databases from inception until February 2020 for articles that used machine learning for diagnostic or prognostic purposes of OSCC. Eligibility criteria: Only original studies that examined the application of machine learning models for prognostic and/or diagnostic purposes were considered. Data extraction: Independent extraction of articles was done by two researchers (A.R. & O.Y) using predefine study selection criteria. We used the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) in the searching and screening processes. We also used Prediction model Risk of Bias Assessment Tool (PROBAST) for assessing the risk of bias (ROB) and quality of included studies. Results: A total of 41 studies were published to have used machine learning to aid in the diagnosis/or prognosis of OSCC. The majority of these studies used the support vector machine (SVM) and artificial neural network (ANN) algorithms as machine learning techniques. Their specificity ranged from 0.57 to 1.00, sensitivity from 0.70 to 1.00, and accuracy from 63.4 % to 100.0 % in these studies. The main limitations and concerns can be grouped as either the challenges inherent to the science of machine learning or relating to the clinical implementations. Conclusion: Machine learning models have been reported to show promising performances for diagnostic and prognostic analyses in studies of oral cancer. These models should be developed to further enhance explainability, interpretability, and externally validated for generalizability in order to be safely integrated into daily clinical practices. Also, regulatory frameworks for the adoption of these models in clinical practices are necessary.Peer reviewe

Specific issues concerning the management of patients on the waiting list and after liver transplantation

The present document is a second contribution collecting the recommendations of an expert panel of transplant hepatologists appointed by the Italian Association for the Study of the Liver (AISF) concerning the management of certain aspects of liver transplantation, including: the issue of prompt referral; the management of difficult candidates; malnutrition; living related liver transplants; hepatocellular carcinoma; and the role of direct acting antiviral agents before and after transplantation. The statements on each topic were approved by participants at the AISF Transplant Hepatology Expert Meeting organized by the Permanent Liver Transplant Commission in Mondello on 12-13 May 2017. They are graded according to the GRADE grading system

The Impact of Histopathological Features on the Prognosis of Oral Squamous Cell Carcinoma : A Comprehensive Review and Meta-Analysis

ObjectiveOver many decades, studies on histopathological features have not only presented high-level evidence of contribution for treatment directions and prognosis of oral squamous cell carcinoma (OSCC) but also provided inconsistencies, making clinical application difficult. The 8th TNM staging system of OSCC has acknowledged the importance of some histopathological features, by incorporating depth of invasion (DOI) to T category and extranodal extension (ENE) to N category. The aim of this systematic review with meta-analysis is to determine the most clinically relevant histopathological features for risk assessment and treatment planning of OSCC and to elucidate gaps in the literature. MethodsA systematic review was conducted using PRISMA guidelines, and the eligibility criteria were based on population, exposure, comparison, outcome, and study type (PECOS). PubMed, Cochrane, Scopus, and Web of Science were searched for articles exploring the impact of histopathological features on OSCC outcomes with Cox multivariate analysis. Pooled data were subjected to an inverse variance method with random effects or fixed effect model, and the risk of bias was evaluated using quality in prognosis studies (QUIPS). Quality of evidence was assessed with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria. ResultsThe study included 172 articles published from 1999 to 2021. Meta-analyses confirmed the prognostic potential of DOI, ENE, perineural invasion, lymphovascular invasion, and involvement of the surgical margins and brought promising results for the association of bone invasion, tumor thickness, and pattern of invasion with increased risk for poor survival. Although with a small number of studies, the results also revealed a clinical significance of tumor budding and tumor-stroma ratio on predicted survival of patients with OSCC. Most of the studies were considered with low or moderate risk of bias, and the certainty in evidence varied from very low to high. ConclusionOur results confirm the potential prognostic usefulness of many histopathological features and highlight the promising results of others; however, further studies are advised to apply consistent designs, filling in the literature gaps to the pertinence of histopathological markers for OSCC prognosis. Systematic Review RegistrationInternational Prospective Register of Systematic Reviews (PROSPERO), identifier CRD42020219630.Peer reviewe

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.Comment: (1) Paper: 33 pages, 4 figures, 1 table; (2) SUPP info: 41 pages, 7 figures, 8 table

Patterns of injury and violence in Yaoundé Cameroon: an analysis of hospital data.

BackgroundInjuries are quickly becoming a leading cause of death globally, disproportionately affecting sub-Saharan Africa, where reports on the epidemiology of injuries are extremely limited. Reports on the patterns and frequency of injuries are available from Cameroon are also scarce. This study explores the patterns of trauma seen at the emergency ward of the busiest trauma center in Cameroon's capital city.Materials and methodsAdministrative records from January 1, 2007, through December 31, 2007, were retrospectively reviewed; information on age, gender, mechanism of injury, and outcome was abstracted for all trauma patients presenting to the emergency ward. Univariate analysis was performed to assess patterns of injuries in terms of mechanism, date, age, and gender. Bivariate analysis was used to explore potential relationships between demographic variables and mechanism of injury.ResultsA total of 6,234 injured people were seen at the Central Hospital of Yaoundé's emergency ward during the year 2007. Males comprised 71% of those injured, and the mean age of injured patients was 29 years (SD = 14.9). Nearly 60% of the injuries were due to road traffic accidents, 46% of which involved a pedestrian. Intentional injuries were the second most common mechanism of injury (22.5%), 55% of which involved unarmed assault. Patients injured in falls were more likely to be admitted to the hospital (p < 0.001), whereas patients suffering intentional injuries and bites were less likely to be hospitalized (p < 0.001). Males were significantly more likely to be admitted than females (p < 0.001)DiscussionPatterns in terms of age, gender, and mechanism of injury are similar to reports from other countries from the same geographic region, but the magnitude of cases reported is high for a single institution in an African city the size of Yaoundé. As the burden of disease is predicted to increase dramatically in sub-Saharan Africa, immediate efforts in prevention and treatment in Cameroon are strongly warranted

Integrated out-of-hours care arrangements in England: observational study of progress towards single call access via NHS Direct and impact on the wider health system

Objectives: To assess the extent of service integration achieved within general practice cooperatives and NHS Direct sites participating in the Department of Health’s national “Exemplar Programme” for single call access to out-of-hours care via NHS Direct. To assess the impact of integrated out-of-hours care arrangements upon general practice cooperatives and the wider health system (use of emergency departments, 999 ambulance services, and minor injuries units). Design: Observational before and after study of demand, activity, and trends in the use of other health services. Setting: Thirty four English general practice cooperatives with NHS Direct partners (“exemplars”) of which four acted as “case exemplars”. Also 10 control cooperatives for comparison. Main Outcome Measures: Extent of integration achieved (defined as the proportion of hours and the proportion of general practice patients covered by integrated arrangements), patterns of general practice cooperative demand and activity and trends in use of the wider health system in the first year. Results: Of 31 distinct exemplars 21 (68%) integrated all out-of-hours call management by March 2004. Nine (29%) established single call access for all patients. In the only case exemplar where direct comparison was possible, cooperative nurse telephone triage before integration completed a higher proportion of calls with telephone advice than did NHS Direct afterwards (39% v 30%; p<0.0001). The proportion of calls completed by NHS Direct telephone advice at other sites was lower. There is evidence for transfer of demand from case exemplars to 999 ambulance services. A downturn in overall demand for care seen in two case exemplars was also seen in control sites. Conclusion: The new model of out-of-hours care was implemented in a variety of settings across England by new partnerships between general practice cooperatives and NHS Direct. Single call access was not widely implemented and most patients needed to make at least two telephone calls to contact the service. In the first year, integration may have produced some reduction in total demand, but this may have been accompanied by shifts from one part of the local health system to another. NHS Direct demonstrated capability in handling calls but may not currently have sufficient capacity to support national implementation

Metodi statistici per la stima di profili di rischio personalizzati basati sulla medicina di precisione del cancro nei pazienti oncologici

Precision medicine is beginning to emerge as a well-defined discipline with specific goals, areas of focus, and tailored methodology. Specifically, the primary goal is to discover treatment rules that leverage heterogeneity to improve clinical decision making in a manner that is reproducible, generalizable, and adaptable as needed. This endeavor spans a broad range of scientific areas including drug discovery, genetics/genomics, health communication, and causal inference, all in support of evidence-based, i.e., data-driven, decision making. Precision Medicine allows patients to be discriminated according to their level of risk (e.g. low or high) and identifies subgroups of patients according to their characteristics in order to assign the treatment to those who are likely to benefit. Statistics research in precision medicine is broadly focused on methodological development for estimation of and inference for treatment regimens that maximize some cumulative clinical outcome. The process for using statistical inference to establish personalized treatment strategies requires specific techniques for data-analysis that optimize the combination of competing therapies with candidate genetic features and characteristics of the patient and disease. The present dissertation focuses on the implementation and application of statistical methods for establishing optimal treatment rules for personalized medicine and discuss specific examples in various medical contexts with oncology as an emphasis. I have focused my research activity mainly in the study of the following topics. 1) Statistical methods to analyze continuous biomarkers. Several approaches were considered according to the design of study: from classical approach - median or mean value, percentiles, optimal cut-point identified by means standard receiver operating characteristic (ROC) analysis-to more complex analysis - time-dependent ROC, conditional inferential tree and subpopulation Treatment Effect Pattern (STEPP) method. 2) Statistical methods for time-to-event endpoints. Competing risks occur commonly in medical research. In the analysis of competing risks data, methods of standard survival analysis lead to incorrect and biased results. In the presence of competing risks, data analysis has to be performed including methods to calculate the cumulative incidence of an event of interest, to compare cumulative incidence curves in the presence of competing risks, and to perform competing risks regression analysis. 3) Meta-analysis for synthesizing evidence. 4) An important topic reviews to use of several statistical methods that handle the issue of treatment switching. The contribution aims at assessing tamoxifen treatment effect taking into account treatment switches, in order to provide a robust assessment of treatment effect applying causal inference methods. 5) The last topic deals with the use of population-based registry and administrative databases. The objective of this project is to develop an acceptable claims-based algorithm to identify second breast cancer events during a 10-year follow-up through a record-linkage of two data sources:the Friuli Venezia Giulia population based-cancer registry and the administrative individual-record FVG database.Precision medicine is beginning to emerge as a well-defined discipline with specific goals, areas of focus, and tailored methodology. Specifically, the primary goal is to discover treatment rules that leverage heterogeneity to improve clinical decision making in a manner that is reproducible, generalizable, and adaptable as needed. This endeavor spans a broad range of scientific areas including drug discovery, genetics/genomics, health communication, and causal inference, all in support of evidence-based, i.e., data-driven, decision making. Precision Medicine allows patients to be discriminated according to their level of risk (e.g. low or high) and identifies subgroups of patients according to their characteristics in order to assign the treatment to those who are likely to benefit. Statistics research in precision medicine is broadly focused on methodological development for estimation of and inference for treatment regimens that maximize some cumulative clinical outcome. The process for using statistical inference to establish personalized treatment strategies requires specific techniques for data-analysis that optimize the combination of competing therapies with candidate genetic features and characteristics of the patient and disease. The present dissertation focuses on the implementation and application of statistical methods for establishing optimal treatment rules for personalized medicine and discuss specific examples in various medical contexts with oncology as an emphasis. I have focused my research activity mainly in the study of the following topics. 1) Statistical methods to analyze continuous biomarkers. Several approaches were considered according to the design of study: from classical approach - median or mean value, percentiles, optimal cut-point identified by means standard receiver operating characteristic (ROC) analysis-to more complex analysis - time-dependent ROC, conditional inferential tree and subpopulation Treatment Effect Pattern (STEPP) method. 2) Statistical methods for time-to-event endpoints. Competing risks occur commonly in medical research. In the analysis of competing risks data, methods of standard survival analysis lead to incorrect and biased results. In the presence of competing risks, data analysis has to be performed including methods to calculate the cumulative incidence of an event of interest, to compare cumulative incidence curves in the presence of competing risks, and to perform competing risks regression analysis. 3) Meta-analysis for synthesizing evidence. 4) An important topic reviews to use of several statistical methods that handle the issue of treatment switching. The contribution aims at assessing tamoxifen treatment effect taking into account treatment switches, in order to provide a robust assessment of treatment effect applying causal inference methods. 5) The last topic deals with the use of population-based registry and administrative databases. The objective of this project is to develop an acceptable claims-based algorithm to identify second breast cancer events during a 10-year follow-up through a record-linkage of two data sources:the Friuli Venezia Giulia population based-cancer registry and the administrative individual-record FVG database

ENDOMET database – A means to identify novel diagnostic and prognostic tools for endometriosis

Endometriosis is a common benign hormone reliant inflammatory gynecological disease that affects fertile aged women and has a considerable economic impact on healthcare systems. Symptoms include intense menstrual pain, persistent pelvic pain, and infertility. It is defined by the existence of endometrium-like tissue developing in ectopic locations outside the uterine cavity and inflammation in the peritoneal cavity. Endometriosis presents with multifactorial etiology, and despite extensive research the etiology is still poorly understood. Diagnostic delay from the onset of the disease to when a conclusive diagnosis is reached is between 7–12 years. There is no known cure, although symptoms can be improved with hormonal medications (which often have multiple side effects and prevent pregnancy), or through surgery which carries its own risk. Current non-invasive tools for diagnosis are not sufficiently dependable, and a definite diagnosis is achieved through laparoscopy or laparotomy. This study was based on two prospective cohorts: The ENDOMET study, including 137 endometriosis patients scheduled for surgery and 62 healthy women, and PROENDO that included 138 endometriosis patients and 33 healthy women. Our long-term goal with the current study was to support the discovery of innovative new tools for efficient diagnosis of endometriosis as well as tools to further understand the etiology and pathogenesis of the disease. We set about achieving this goal by creating a database, EndometDB, based on a relational data model, implemented with PostgreSQL programming language. The database allows e.g., for the exploration of global genome-wide expression patterns in the peritoneum, endometrium, and in endometriosis lesions of endometriosis patients as well as in the peritoneum and endometrium of healthy control women of reproductive age. The data collected in the EndometDB was also used for the development and validation of a symptom and biomarker-based predictive model designed for risk evaluation and early prediction of endometriosis without invasive diagnostic methods. Using the data in the EndometDB we discovered that compared with the eutopic endometrium, the WNT- signaling pathway is one of the molecular pathways that undergo strong changes in endometriosis. We then evaluated the potential role for secreted frizzled-related protein 2 (SFRP-2, a WNT-signaling pathway modulator), in improving endometriosis lesion border detection. The SFRP-2 expression visualizes the lesion better than previously used markers and can be used to better define lesion size and that the surgical excision of the lesions is complete.ENDOMET tietokanta – Keino tunnistaa uusi diagnostinen ja ennustava työkalu endometrioosille Endometrioosi on yleinen hyvänlaatuinen, hormoneista riippuvainen tulehduksellinen lisääntymisikäisten naisten gynekologinen sairaus, joka kuormittaa terveydenhuoltojärjestelmää merkittävästi. Endometrioositaudin oireita ovat mm. voimakas kuukautiskipu, jatkuva lantion alueen kipu ja hedelmättömyys. Sairaus määritellään kohdun limakalvon kaltaisen kudoksen esiintymisenä kohdun ulkopuolella sekä siihen liittyvänä vatsakalvon tulehduksena. Endometrioosin etiologia on monitahoinen, ja laajasta tutkimuksesta huolimatta edelleen huonosti tunnettu. Kesto taudin puhkeamisesta lopullisen diagnoosin saamiseen on usein jopa 7–12 vuotta. Sairauteen ei tunneta parannuskeinoa, mutta oireita voidaan lievittää esimerkiksi hormonaalisilla lääkkeillä (joilla on usein monia sivuvaikutuksia ja jotka estävät raskauden) tai leikkauksella, johon liittyy omat tunnetut riskit. Nykyiset ei-invasiiviset diagnoosityökalut eivät ole riittävän luotettavia sairauden tunnistamiseen, ja varma endometrioosin diagnoosi saavutetaan laparoskopian tai laparotomian avulla. Tämä tutkimus perustui kahteen prospektiiviseen kohorttiin: ENDOMET-tutkimuk-seen, johon osallistui 137 endometrioosipotilasta ja 62 terveellistä naista, sekä PROENDO-tutkimukseen, johon osallistui 138 endometrioosipotilasta ja 33 terveellistä naista. Tässä tutkimuksessa pitkän aikavälin tavoitteemme oli löytää uusia työkalujen endometrioosin diagnosointiin, sekä ymmärtää endometrioosin etiologiaa ja patogeneesiä. Ensimmäisessä vaiheessa loimme EndometDB –tietokannan PostgreSQL-ohjelmointi-kielellä. Tämän osittain avoimeen käyttöön vapautetun tietokannan avulla voidaan tutkia genomin, esimerkiksi kaikkien tunnettujen geenien ilmentymistä peritoneumissa, endo-metriumissa ja endometrioosipotilaiden endometrioosileesioissa EndometDB-tietokantaan kerättyjä tietoja käytettiin oireiden ja biomarkkeripohjaisen ennustemallin kehittämiseen ja validointiin. Malli tuottaa riskinarvioinnin endometrioositaudin varhaiseen ennustamiseen ilman laparoskopiaa. Käyttäen EndometDB-tietokannan tietoja havaitsimme, että endo-metrioositautikudoksessa tapahtui voimakkaita geeni-ilmentymisen muutoksia erityisesti geeneissä, jotka liittyvät WNT-signalointireitin säätelyyn. Keskeisin löydös oli, että SFRP-2 proteiinin ilmentyminen oli huomattavasti koholla endometrioosikudoksessa ja SFRP-2 proteiinin immunohistokemiallinen värjäys erottaa endometrioosin tautikudoksen terveestä kudoksesta aiempia merkkiaineita paremmin. Löydetyllä menetelmällä voidaan siten selvittää tautikudoksen laajuus ja tarvittaessa osoittaa, että leikkauksella on kyetty poistamaan koko sairas kudos