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The Journal of Pathology. Clinical... May 2021The SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) 2013 Statement was developed to provide guidance for inclusion of key methodological... (Meta-Analysis)
Meta-Analysis
The SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) 2013 Statement was developed to provide guidance for inclusion of key methodological components in clinical trial protocols. However, these standards do not include guidance specific to pathology input in clinical trials. This systematic review aims to synthesise existing recommendations specific to pathology practice in clinical trials for implementation in trial protocol design. Articles were identified from database searches and deemed eligible for inclusion if they contained: (1) guidance and/or a checklist, which was (2) pathology-related, with (3) content relevant to clinical trial protocols or could influence a clinical trial protocol design from a pathology perspective and (4) were published in 1996 or later. The quality of individual papers was assessed using the AGREE-GRS (Appraisal of Guidelines for REsearch & Evaluation - Global Rating Scale) tool, and the confidence in cumulative evidence was evaluated using the GRADE-CERQual (Grading of Recommendations Assessment, Development and Evaluation-Confidence in Evidence from Reviews of Qualitative research) approach. Extracted recommendations were synthesised using the best fit framework method, which includes thematic analysis followed by a meta-aggregative approach to synthesis within the framework. Of the 10 184 records screened and 199 full-text articles reviewed, only 40 guidance resources met the eligibility criteria for inclusion. Recommendations extracted from 22 guidance documents were generalisable enough for data synthesis. Seven recommendation statements were synthesised as follows: (1) multidisciplinary collaboration in trial design with early involvement of pathologists, particularly with respect to the use of biospecimens and associated biomarker/analytical assays and in the evaluation of pathology-related parameters; (2) funding and training for personnel undertaking trial work; (3) selection of an accredited laboratory with suitable facilities to undertake scheduled work; (4) quality assurance of pathology-related parameters; (5) transparent reporting of pathology-related parameters; (6) policies regarding informatics and tracking biospecimens across trial sites; and (7) informed consent for specimen collection and retention for future research.
Topics: Biomarkers; Biopsy; Clinical Trials as Topic; Humans; Pathology, Clinical; Pathology, Molecular; Practice Guidelines as Topic; Predictive Value of Tests; Research Design; Treatment Outcome
PubMed: 33635586
DOI: 10.1002/cjp2.199 -
Journal of Pathology Informatics Dec 2024Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important... (Review)
Review
Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review.
Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important evidence of their generalizability. The aim of this systematic review was to assess the performance of externally validated ML models based on histopathology images for diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer. A systematic search of MEDLINE, EMBASE, CINAHL, IEEE, MICCAI, and SPIE conferences was performed for studies published between January 2010 and February 2022. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed, and the results were narratively described. Of the 2011 non-duplicated citations, 8 journal articles and 2 conference proceedings met inclusion criteria. Three studies externally validated ML models for diagnosis, 4 for classification, 2 for prognosis, and 1 for both classification and prognosis. Most studies used Convolutional Neural Networks and one used logistic regression algorithms. For diagnostic/classification models, the most common performance metrics reported in the EV were accuracy and area under the curve, which were greater than 87% and 90%, respectively, using pathologists' annotations/diagnoses as ground truth. The hazard ratios in the EV of prognostic ML models were between 1.7 (95% CI, 1.2-2.6) and 1.8 (95% CI, 1.3-2.7) to predict distant disease-free survival; 1.91 (95% CI, 1.11-3.29) for recurrence, and between 0.09 (95% CI, 0.01-0.70) and 0.65 (95% CI, 0.43-0.98) for overall survival, using clinical data as ground truth. Despite EV being an important step before the clinical application of a ML model, it hasn't been performed routinely. The large variability in the training/validation datasets, methods, performance metrics, and reported information limited the comparison of the models and the analysis of their results. Increasing the availability of validation datasets and implementing standardized methods and reporting protocols may facilitate future analyses.
PubMed: 38089005
DOI: 10.1016/j.jpi.2023.100348 -
PeerJ. Computer Science 2024Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood...
BACKGROUND
Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking a holistic view like segmentation, classification, feature extraction, dataset utilization, evaluation matrices, .
METHODOLOGY
This survey aims to provide a comprehensive and systematic review of existing literature and research work in the field of blood image analysis using deep learning techniques. It particularly focuses on medical image processing techniques and deep learning algorithms that excel in the morphological characterization of WBCs and RBCs. The review is structured to cover four main areas: segmentation techniques, classification methodologies, descriptive feature selection, evaluation parameters, and dataset selection for the analysis of WBCs and RBCs.
RESULTS
Our analysis reveals several interesting trends and preferences among researchers. Regarding dataset selection, approximately 50% of research related to WBC segmentation and 60% for RBC segmentation opted for manually obtaining images rather than using a predefined dataset. When it comes to classification, 45% of the previous work on WBCs chose the ALL-IDB dataset, while a significant 73% of researchers focused on RBC classification decided to manually obtain images from medical institutions instead of utilizing predefined datasets. In terms of feature selection for classification, morphological features were the most popular, being chosen in 55% and 80% of studies related to WBC and RBC classification, respectively.
CONCLUSION
The diagnostic accuracy for blood-related diseases like leukemia, anemia, lymphoma, and thalassemia can be significantly enhanced through the effective use of CAD techniques, which have evolved considerably in recent years. This survey provides a broad and in-depth review of the techniques being employed, from image segmentation to classification, feature selection, utilization of evaluation matrices, and dataset selection. The inconsistency in dataset selection suggests a need for standardized, high-quality datasets to strengthen the diagnostic capabilities of these techniques further. Additionally, the popularity of morphological features indicates that future research could further explore and innovate in this direction.
PubMed: 38435563
DOI: 10.7717/peerj-cs.1813 -
Forensic Science International Nov 2023Dog bites pose a significant global public health issue and are the most common type of injury caused by animals. While most dog bites result in minor harm, they can... (Review)
Review
Dog bites pose a significant global public health issue and are the most common type of injury caused by animals. While most dog bites result in minor harm, they can also lead to severe or even fatal consequences. In cases involving serious injury or death, forensic pathologists investigate various aspects, including the crime scene, the injuries sustained by the victim, and the characteristics of the dog suspected to have caused the bite. The aim of this study is to provide a systematic review of the literature on the medical-legal implications of dog bites in forensic practice, in order to recognize the dog bite victim features, the injuries and their consequences related to, and to identify the offending dogs. The literature search was performed using PubMed, Scopus and Web of Science from January 1980 to March 2023. Eligible studies have investigated issues of interest to forensic medicine about dog bites to humans. A total of 116 studies met the inclusion criteria and were included in the review and they were organized and discussed by issue of interest (biting dog features, dog bite victim features, anatomical distribution of dog bites, injuries related to dog bites, cause of death, bite features, dog identification and post-mortem dog depredation). The findings of this systematic review highlight the importance of bite mark analysis in reconstructing the events leading to the attack and identifying the dog responsible. In medical forensic evaluations of dog bite cases, a multidisciplinary approach is crucial. This approach involves thorough analysis of the crime scene, identification of risk factors, examination of dog characteristics, and assessment of the victim's injuries. By combining expertise from both human and veterinary forensic fields, a comprehensive understanding can be achieved in dog bite cases.
Topics: Humans; Dogs; Animals; Bites and Stings; Forensic Medicine; Crime; Risk Factors; Autopsy
PubMed: 37783138
DOI: 10.1016/j.forsciint.2023.111849 -
The Cochrane Database of Systematic... Sep 2022Autism spectrum disorder is a neurodevelopmental disorder characterised by social communication difficulties, restricted interests and repetitive behaviours. The... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Autism spectrum disorder is a neurodevelopmental disorder characterised by social communication difficulties, restricted interests and repetitive behaviours. The clinical pathway for children with a diagnosis of autism spectrum disorder is varied, and current research suggests some children may not continue to meet diagnostic criteria over time.
OBJECTIVES
The primary objective of this review was to synthesise the available evidence on the proportion of preschool children who have a diagnosis of autism spectrum disorder at baseline (diagnosed before six years of age) who continue to meet diagnostic criteria at follow-up one or more years later (up to 19 years of age).
SEARCH METHODS
We searched MEDLINE, Embase, PsycINFO, and eight other databases in October 2017 and ran top-up searches up to July 2021. We also searched reference lists of relevant systematic reviews.
SELECTION CRITERIA
Two review authors independently assessed prospective and retrospective follow-up studies that used the same measure and process within studies to diagnose autism spectrum disorder at baseline and follow-up. Studies were required to have at least one year of follow-up and contain at least 10 participants. Participants were all aged less than six years at baseline assessment and followed up before 19 years of age.
DATA COLLECTION AND ANALYSIS
We extracted data on study characteristics and the proportion of children diagnosed with autism spectrum disorder at baseline and follow-up. We also collected information on change in scores on measures that assess the dimensions of autism spectrum disorder (i.e. social communication and restricted interests and repetitive behaviours). Two review authors independently extracted data on study characteristics and assessed risk of bias using a modified quality in prognosis studies (QUIPS) tool. We conducted a random-effects meta-analysis or narrative synthesis, depending on the type of data available. We also conducted prognostic factor analyses to explore factors that may predict diagnostic outcome.
MAIN RESULTS
In total, 49 studies met our inclusion criteria and 42 of these (11,740 participants) had data that could be extracted. Of the 42 studies, 25 (60%) were conducted in North America, 13 (31%) were conducted in Europe and the UK, and four (10%) in Asia. Most (52%) studies were published before 2014. The mean age of the participants was 3.19 years (range 1.13 to 5.0 years) at baseline and 6.12 years (range 3.0 to 12.14 years) at follow-up. The mean length of follow-up was 2.86 years (range 1.0 to 12.41 years). The majority of the children were boys (81%), and just over half (60%) of the studies primarily included participants with intellectual disability (intelligence quotient < 70). The mean sample size was 272 (range 10 to 8564). Sixty-nine per cent of studies used one diagnostic assessment tool, 24% used two tools and 7% used three or more tools. Diagnosis was decided by a multidisciplinary team in 41% of studies. No data were available for the outcomes of social communication and restricted and repetitive behaviours and interests. Of the 42 studies with available data, we were able to synthesise data from 34 studies (69% of all included studies; n = 11,129) in a meta-analysis. In summary, 92% (95% confidence interval 89% to 95%) of participants continued to meet diagnostic criteria for autism spectrum disorder from baseline to follow-up one or more years later; however, the quality of the evidence was judged as low due to study limitations and inconsistency. The majority of the included studies (95%) were rated at high risk of bias. We were unable to explore the outcomes of change in social communication and restricted and repetitive behaviour and interests between baseline and follow-up as none of the included studies provided separate domain scores at baseline and follow-up. Details on conflict of interest were reported in 24 studies. Funding support was reported by 30 studies, 12 studies omitted details on funding sources and two studies reported no funding support. Declared funding sources were categorised as government, university or non-government organisation or charity groups. We considered it unlikely funding sources would have significantly influenced the outcomes, given the nature of prognosis studies.
AUTHORS' CONCLUSIONS
Overall, we found that nine out of 10 children who were diagnosed with autism spectrum disorder before six years of age continued to meet diagnostic criteria for autism spectrum disorder a year or more later, however the evidence was uncertain. Confidence in the evidence was rated low using GRADE, due to heterogeneity and risk of bias, and there were few studies that included children diagnosed using a current classification system, such as the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) or the eleventh revision of the International Classification of Diseases (ICD-11). Future studies that are well-designed, prospective and specifically assess prognosis of autism spectrum disorder diagnoses are needed. These studies should also include contemporary diagnostic assessment methods across a broad range of participants and investigate a range of relevant prognostic factors.
Topics: Adult; Autism Spectrum Disorder; Child; Child, Preschool; Female; Humans; Infant; Male; Prognosis; Prospective Studies; Retrospective Studies; Schools; Young Adult
PubMed: 36169177
DOI: 10.1002/14651858.CD012749.pub2 -
Forensic Science International Jun 2024Cardiac implantable electronic devices (CIED) are a heterogeneous group of medical devices with increasingly sophisticated diagnostic capabilities, which could be... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Cardiac implantable electronic devices (CIED) are a heterogeneous group of medical devices with increasingly sophisticated diagnostic capabilities, which could be exploited in forensic investigations. However, current guidelines are lacking clear recommendations on the topic. The first aim of this systematic review is to provide an updated assessment of the role of postmortem CIED interrogation, and to give practical recommendations, which can be used in daily practice. Secondly, the authors aim to determine the rates of postmortem CIED interrogation and autopsy investigations, the type of final rhythm detected close to death (with a focus on the significance of documented arrhythmias), as well as the role of postmortem CIED interrogation in the determination of final cause/time of death, and any potentially fatal device malfunctions.
METHODS
A systematic search in MEDLINE and Scopus aiming to identify reports concerning postmortem human CIED interrogation was performed, including a systematic screening of reference lists. Case reports, letters to the editors, commentaries, review articles or guidelines were excluded, along with studies related to cardiac devices other than CIED. All data were pooled and analyzed using fixed-effects meta-analysis models, and the I statistic was used to assess heterogeneity.
RESULTS
A total of 25 articles were included in the systematic review, enrolling 3194 decedent CIED carriers. Ten studies (40%) had a 100% autopsy rate, whereas in further 6 studies autopsy findings were variably reported; CIED interrogation was available from 22 studies (88%), and it was never performed prior to autopsy. The overall rate of successful postmortem CIED interrogation was 89%, with high heterogeneity among studies, mainly due to device deactivation/battery discharge. Twenty-four percent of CIED carriers experienced sudden cardiac death (SCD), whereas non-sudden cardiac and non-cardiac death (NSCD, NCD) were reported in 37% and 30% of decedents, respectively. Ventricular tachyarrhythmias were recorded in 34% of overall successfully interrogated CIED, and in 62% of decedents who experienced a SCD; of all ventricular tachyarrhythmias recorded, 40% was found in NSCD or NCD. A clear interpretation of the etiological role of recorded arrhythmias in the causation of death required integration with autopsy findings. Overall, potentially fatal device malfunctions were detected in 12% of cases.
CONCLUSIONS
Postmortem CIED interrogation is a valuable tool for the determination of the cause of death, and may complement autopsy. Forensic pathologists need to know the potential utility, pitfalls, and limitations of this diagnostic examination to make this tool as much reliable as possible.
Topics: Humans; Arrhythmias, Cardiac; Defibrillators, Implantable; Equipment Failure; Pacemaker, Artificial; Cause of Death; Guidelines as Topic; Autopsy
PubMed: 38714107
DOI: 10.1016/j.forsciint.2024.112001 -
Frontiers in Oncology 2021Many diseases are accompanied by changes in certain biochemical indicators called biomarkers in cells or tissues. A variety of biomarkers, including proteins, nucleic... (Review)
Review
Many diseases are accompanied by changes in certain biochemical indicators called biomarkers in cells or tissues. A variety of biomarkers, including proteins, nucleic acids, antibodies, and peptides, have been identified. Tumor biomarkers have been widely used in cancer risk assessment, early screening, diagnosis, prognosis, treatment, and progression monitoring. For example, the number of circulating tumor cell (CTC) is a prognostic indicator of breast cancer overall survival, and tumor mutation burden (TMB) can be used to predict the efficacy of immune checkpoint inhibitors. Currently, clinical methods such as polymerase chain reaction (PCR) and next generation sequencing (NGS) are mainly adopted to evaluate these biomarkers, which are time-consuming and expansive. Pathological image analysis is an essential tool in medical research, disease diagnosis and treatment, functioning by extracting important physiological and pathological information or knowledge from medical images. Recently, deep learning-based analysis on pathological images and morphology to predict tumor biomarkers has attracted great attention from both medical image and machine learning communities, as this combination not only reduces the burden on pathologists but also saves high costs and time. Therefore, it is necessary to summarize the current process of processing pathological images and key steps and methods used in each process, including: (1) pre-processing of pathological images, (2) image segmentation, (3) feature extraction, and (4) feature model construction. This will help people choose better and more appropriate medical image processing methods when predicting tumor biomarkers.
PubMed: 34900711
DOI: 10.3389/fonc.2021.763527 -
Cancers Dec 2022The rise of Artificial Intelligence (AI) has shown promising performance as a support tool in clinical pathology workflows. In addition to the well-known interobserver... (Review)
Review
The rise of Artificial Intelligence (AI) has shown promising performance as a support tool in clinical pathology workflows. In addition to the well-known interobserver variability between dermatopathologists, melanomas present a significant challenge in their histological interpretation. This study aims to analyze all previously published studies on whole-slide images of melanocytic tumors that rely on deep learning techniques for automatic image analysis. Embase, Pubmed, Web of Science, and Virtual Health Library were used to search for relevant studies for the systematic review, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Articles from 2015 to July 2022 were included, with an emphasis placed on the used artificial intelligence methods. Twenty-eight studies that fulfilled the inclusion criteria were grouped into four groups based on their clinical objectives, including pathologists versus deep learning models ( = 10), diagnostic prediction ( = 7); prognosis ( = 5), and histological features ( = 6). These were then analyzed to draw conclusions on the general parameters and conditions of AI in pathology, as well as the necessary factors for better performance in real scenarios.
PubMed: 36612037
DOI: 10.3390/cancers15010042 -
Nursing Forum Nov 2022To identify the current research involving interprofessional collaboration between registered nurses (RNs) and speech language pathologists (SLPs) in healthcare and...
AIMS AND OBJECTIVE
To identify the current research involving interprofessional collaboration between registered nurses (RNs) and speech language pathologists (SLPs) in healthcare and educational settings.
BACKGROUND
As the complexity of healthcare increases, the need for active interprofessional collaboration between RNs and SLPs grows. A review of the literature revealed no systematic reviews currently exist about interprofessional collaborative studies between RNs and SLPs.
DESIGN
Researchers conducted a scoping review using PRISMA guidelines.
METHODS
Online databases were used to identify qualitative and quantitative research studies written in English and conducted between 2011 and 2020. Databases included Academic Search Ultimate, ASHA Wire, CINAHL, Cochrane Database of Systematic Reviews, ERIC, MEDLINE, PubMed, PsycINFO, and SEMANTIC SCHOLAR. The studies needed to focus on the interprofessional collaboration between RNs and SLPs or students in these professions.
FINDINGS
Of the 128 sources, only six studies met scoping review criteria. The primary focus of three studies was an evaluation of interprofessional education activities between nursing, speech language pathology, and other health profession students. One study explored interprofessional education in clinical practice between RNs and SLPs. Two studies explored interprofessional collaboration in the clinical setting.
CONCLUSION
More research is needed that investigates interprofessional collaboration and practice of RNs and SLPs in the healthcare setting.
RELEVANCE TO CLINICAL PRACTICE
This review identified the need for RNs and SLPs to work effectively as interprofessional teams are important in improving patient outcomes.
Topics: Humans; Pathologists; Speech; Speech-Language Pathology; Delivery of Health Care; Nurses
PubMed: 36161720
DOI: 10.1111/nuf.12802 -
Clinical Microbiology and Infection :... Aug 2022Many postmortem studies address the cardiovascular effects of COVID-19 and provide valuable information, but are limited by their small sample size. (Review)
Review
BACKGROUND
Many postmortem studies address the cardiovascular effects of COVID-19 and provide valuable information, but are limited by their small sample size.
OBJECTIVES
The aim of this systematic review is to better understand the various aspects of the cardiovascular complications of COVID-19 by pooling data from a large number of autopsy studies.
DATA SOURCES
We searched the online databases Ovid EBM Reviews, Ovid Embase, Ovid Medline, Scopus, and Web of Science for concepts of autopsy or histopathology combined with COVID-19, published between database inception and February 2021. We also searched for unpublished manuscripts using the medRxiv services operated by Cold Spring Harbor Laboratory.
STUDY ELIGIBILITY CRITERIA
Articles were considered eligible for inclusion if they reported human postmortem cardiovascular findings among individuals with a confirmed SARS coronavirus type 2 (CoV-2) infection.
PARTICIPANTS
Confirmed COVID-19 patients with post-mortem cardiovascular findings.
INTERVENTIONS
None.
METHODS
Studies were individually assessed for risk of selection, detection, and reporting biases. The median prevalence of different autopsy findings with associated interquartile ranges (IQRs).
RESULTS
This review cohort contained 50 studies including 548 hearts. The median age of the deceased was 69 years. The most prevalent acute cardiovascular findings were myocardial necrosis (median: 100.0%; IQR, 20%-100%; number of studies = 9; number of patients = 64) and myocardial oedema (median: 55.5%; IQR, 19.5%-92.5%; number of studies = 4; number of patients = 46). The median reported prevalence of extensive, focal active, and multifocal myocarditis were all 0.0%. The most prevalent chronic changes were myocyte hypertrophy (median: 69.0%; IQR, 46.8%-92.1%) and fibrosis (median: 35.0%; IQR, 35.0%-90.5%). SARS-CoV-2 was detected in the myocardium with median prevalence of 60.8% (IQR 40.4-95.6%).
CONCLUSIONS
Our systematic review confirmed the high prevalence of acute and chronic cardiac pathologies in COVID-19 and SARS-CoV-2 cardiac tropism, as well as the low prevalence of myocarditis in COVID-19.
Topics: Aged; Autopsy; COVID-19; Humans; Lung; Myocarditis; SARS-CoV-2
PubMed: 35339672
DOI: 10.1016/j.cmi.2022.03.021