Інтелектуальна система автоматизованої мікроскопії аналізу гістологічних та цитологічних зображень

У статті проведено порівняльний аналіз систем автоматизованої мікроскопії на низькому, середньому та високому рівнях комп’ютерного зору. Спроектовано та програмно реалізовано інтелектуальну систему діагностування. Перевагою системи, порівняно з аналогами, є наявність чотирьох рівнів доступу, адаптивного графічного інтерфейсу для різних типів користувачів, методів автоматичного покращення якості зображення та їх сегментації, модуля для зручної комунікації між користувачами, модуля обліку пацієнтів. Розроблена система забезпечує можливість обробки кількісних та якісних характеристик зображень.Comparative analysis of automated microscopy systems at low, medium and high levels of computer vision is conducted. An intelligent diagnostic system is implemented and programmed. The advantage of the system in comparison with the analogues is the availability of four levels of access, an adaptive graphical interface for different types of users, methods for automatic image quality improvement and segmentation, a module for user-friendly communication, a patients accounting module. The developed system provides the opportunity to process quantitative and qualitative characteristics of images

A Real-Time Screening Alert Improves Patient Recruitment Efficiency

The scarcity of cost-effective patient identification methods represents a significant barrier to clinical research. Research recruitment alerts have been designed to facilitate physician referrals but limited support is available to clinical researchers. We conducted a retrospective data analysis to evaluate the efficacy of a real-time patient identification alert delivered to clinical research coordinators recruiting for a clinical prospective cohort study. Data from log analysis and informal interviews with coordinators were triangulated. Over a 12-month period, 11,295 were screened electronically, 1,449 were interviewed, and 282 were enrolled. The enrollment rates for the alert and two other conventional methods were 4.65%, 2.01%, and 1.34% respectively. A taxonomy of eligibility status was proposed to precisely categorize research patients. Practical ineligibility factors were identified and their correlation with age and gender were analyzed. We conclude that the automatic prescreening alert improves screening efficiency and is an effective aid to clinical research coordinators

Influencing Factors of Clinical Patient Recruitment Systems Design

Clinical patient recruitment (CPR) is a critical function in clinical research. However, there is no holistic design for CPR systems that incorporates functions to support all critical success factors of clinical trial performance. In order to fill this gap, a study based on a literature review and several semi-structured expert interviews was conducted. Existing theory was synthesized with newly found influence factors using categories from CPR theory and factors gathered from literature and experts. The result is a systematization of influence factors of CPR that can be used for derivation of requirements for CPR systems in a subsequent research step or for the purpose of causal modeling

Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis.

OBJECTIVES: 1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop and validate a disease phenotyping algorithm for rheumatoid arthritis using primary care EHRs. METHODS: This study linked routine primary and secondary care EHRs in Wales, UK. A machine learning based scheme was used to identify patients with rheumatoid arthritis from primary care EHRs via the following steps: i) selection of variables by comparing relative frequencies of Read codes in the primary care dataset associated with disease case compared to non-disease control (disease/non-disease based on the secondary care diagnosis); ii) reduction of predictors/associated variables using a Random Forest method, iii) induction of decision rules from decision tree model. The proposed method was then extensively validated on an independent dataset, and compared for performance with two existing deterministic algorithms for RA which had been developed using expert clinical knowledge. RESULTS: Primary care EHRs were available for 2,238,360 patients over the age of 16 and of these 20,667 were also linked in the secondary care rheumatology clinical system. In the linked dataset, 900 predictors (out of a total of 43,100 variables) in the primary care record were discovered more frequently in those with versus those without RA. These variables were reduced to 37 groups of related clinical codes, which were used to develop a decision tree model. The final algorithm identified 8 predictors related to diagnostic codes for RA, medication codes, such as those for disease modifying anti-rheumatic drugs, and absence of alternative diagnoses such as psoriatic arthritis. The proposed data-driven method performed as well as the expert clinical knowledge based methods. CONCLUSION: Data-driven scheme, such as ensemble machine learning methods, has the potential of identifying the most informative predictors in a cost-effective and rapid way to accurately and reliably classify rheumatoid arthritis or other complex medical conditions in primary care EHRs

Three Essays on Enhancing Clinical Trial Subject Recruitment Using Natural Language Processing and Text Mining

Patient recruitment and enrollment are critical factors for a successful clinical trial; however, recruitment tends to be the most common problem in most clinical trials. The success of a clinical trial depends on efficiently recruiting suitable patients to conduct the trial. Every clinical trial research has a protocol, which describes what will be done in the study and how it will be conducted. Also, the protocol ensures the safety of the trial subjects and the integrity of the data collected. The eligibility criteria section of clinical trial protocols is important because it specifies the necessary conditions that participants have to satisfy. Since clinical trial eligibility criteria are usually written in free text form, they are not computer interpretable. To automate the analysis of the eligibility criteria, it is therefore necessary to transform those criteria into a computer-interpretable format. Unstructured format of eligibility criteria additionally create search efficiency issues. Thus, searching and selecting appropriate clinical trials for a patient from relatively large number of available trials is a complex task. A few attempts have been made to automate the matching process between patients and clinical trials. However, those attempts have not fully integrated the entire matching process and have not exploited the state-of-the-art Natural Language Processing (NLP) techniques that may improve the matching performance. Given the importance of patient recruitment in clinical trial research, the objective of this research is to automate the matching process using NLP and text mining techniques and, thereby, improve the efficiency and effectiveness of the recruitment process. This dissertation research, which comprises three essays, investigates the issues of clinical trial subject recruitment using state-of-the-art NLP and text mining techniques. Essay 1: Building a Domain-Specific Lexicon for Clinical Trial Subject Eligibility Analysis Essay 2: Clustering Clinical Trials Using Semantic-Based Feature Expansion Essay 3: An Automatic Matching Process of Clinical Trial Subject Recruitment In essay1, I develop a domain-specific lexicon for n-gram Named Entity Recognition (NER) in the breast cancer domain. The domain-specific dictionary is used for selection and reduction of n-gram features in clustering in eassy2. The domain-specific dictionary was evaluated by comparing it with Systematized Nomenclature of Medicine--Clinical Terms (SNOMED CT). The results showed that it add significant number of new terms which is very useful in effective natural language processing In essay 2, I explore the clustering of similar clinical trials using the domain-specific lexicon and term expansion using synonym from the Unified Medical Language System (UMLS). I generate word n-gram features and modify the features with the domain-specific dictionary matching process. In order to resolve semantic ambiguity, a semantic-based feature expansion technique using UMLS is applied. A hierarchical agglomerative clustering algorithm is used to generate clinical trial clusters. The focus is on summarization of clinical trial information in order to enhance trial search efficiency. Finally, in essay 3, I investigate an automatic matching process of clinical trial clusters and patient medical records. The patient records collected from a prior study were used to test our approach. The patient records were pre-processed by tokenization and lemmatization. The pre-processed patient information were then further enhanced by matching with breast cancer custom dictionary described in essay 1 and semantic feature expansion using UMLS Metathesaurus. Finally, I matched the patient record with clinical trial clusters to select the best matched cluster(s) and then with trials within the clusters. The matching results were evaluated by internal expert as well as external medical expert

Using OncoDoc as a computer-based eligibility screening system to improve accrual onto breast cancer clinical trials.

While clinical trials offer cancer patients the optimum treatment, historical accrual of such patients has not been very successful. OncoDoc is a decision support system designed to provide best therapeutic recommendations for breast cancer patients. Developed as a browsing tool of a knowledge base structured as a decision tree, OncoDoc allows physicians to control the contextual instantiation of patient characteristics to build the best formal equivalent of an actual patient. Used as a computer-based eligibility screening system, depending on whether instantiated patient parameters are matched against guideline knowledge or available clinical trial protocols, it provides either evidence-based therapeutic options or relevant patient-specific clinical trials. Implemented at the Gustave Roussy Institute and routinely used at the point of care during a 4-month period, it significantly improved physician compliance with guideline recommendations and enhanced physician awareness of open trials while increasing patient enrollment to clinical trials by 50%. But, when analyzing reasons of non-accrual of potentially eligible patients, it appeared that physicians' psychological reluctance to refer patients to clinical trials, measured during the experiment at 25%, may not be resolved by the simple dissemination of clinical trial information at the point of care

Presentación de un modelo de decisión Bayesiano para el tratamiento del Carcinoma Ductal In Situ (CDIS) de mama.

El Carcinoma de Mama (CM) es el tumor maligno más frecuente en mujeres y su incidencia aumenta un 2% anual, por ello es uno de los problemas sanitarios más importantes de los países industrializados. Los sistemas nacionales de salud se centran en su diagnóstico precoz para minimizar las consecuencias fatales de la enfermedad, con la realización de mamografías periódicas en las mujeres entre los 40 y 70 años. El desarrollo de estos programas de cribado han demostrado adelantar el diagnóstico del CM un tiempo medio de 1,7 años y reducir la mortalidad un 30% en las pacientes mayores de 40 años, el 30% de los tumores son menores o iguales a 1 cm siendo la probabilidad de afectación ganglionar muy baja (10-18%). Estos resultados se deben a que se ha demostrado que la supervivencia en el CM depende de la limitación tumoral de la enfermedad a la mama. Pero el diagnóstico precoz ha aumentado espectacularmente el diagnóstico de Carcinoma Ductal In Situ (CDIS), cuyo crecimiento se limita al ducto mamario y por lo que se considera un estadio inicial dentro del CM, pero abarca gran variabilidad de lesiones con menor o mayor riesgo de desarrollar un Carcinoma Invasivo (CINV). Según la clasificación de la OMS, el CDIS es un tumor epitelial no invasivo. La clasificación TNM es el sistema de clasificación más utilizado en la práctica clínica habitual debido a su repercusión terapéutica y pronóstica. Dentro de la clasificación TNM el CDIS se considera Tis N0 M0 ya que el CDIS puro no habrá afectación ganglionar ni metástasica. En los casos de CDIS con microinfiltración, la posibilidad de afectación ganglionar ya no es nula, pudiendo presentar una afectación microscópica (pN0(i+), pN1mi) o macroscópica (pN1-N3). El CDIS presenta una Supervivencia Cáncer de Mama Específica (SCME) cercana al 100% a los 10 años, la Recidiva Local (RL) es utilizada como un marcador de fallo en el tratamiento de las pacientes. más de la mitad de las RL son invasivas, disminuyendo de manera estadísticamente significativa la SCME. Los resultados de los diferentes estudios que apoyan que la cirugía conservadora (CC) de la mama no se asocia a un peor pronóstico que técnicas más agresivas, son extrapolables al CDIS y por ello es la técnica quirúrgica mayoritariamente utilizada, aunque en los últimos años se ha observado un aumento de las mastectomía subcutánea y de la profiláctica sbt en pacientes jóvenes. La Biopsia Selectiva del Ganglio Centinela (BSGC) no está justificada de rutina en el CDIS, sólo se recomienda su utilización en el CDIS con microinvasión, en el medible en la MX o RM, y en aquel que requiere una mastectomía, a pesar de estas recomendaciones, se realiza en el 15% de las pacientes. El tratamiento se considera un tratamiento preventivo y se basa en la Radioterapia de toda la mama y la Hormonoterapia, principalmente con Tamoxifeno (TAM) durante 5 años. Existen factores pronósticos que van a determinar la evolución del CM, dependerán tanto de la agresividad del mismo como de la capacidad de respuesta inmunitaria de la paciente. dentro de ellos, los que se han relacionado claramente con la recaída y la supervivencia del CM, el factor pronóstico más importante es la afectación ganglionar disminuyendo significativamente la supervivencia a los 5 años. La expresión de receptores también han demostrado relacionarse con el pronóstico, su positividad conlleva un pronóstico favorable y justifica el tratamiento hormonal, por lo que su determinación es obligatoria en todos los casos y ayuda a clasificar el tumor en fenotipos moleculares que han demostrado predecir la recidiva temprana. Las decisiones en Medicina se basan en las Guías de Práctica Clínica (GPC) que nos proporcionan a los médicos recomendaciones y guías de actuación que nos ayudan a la atención óptima de los pacientes. Sus recomendaciones se basan en la Medicina Basada en la Evidencia (MBE) , que se inició para evitar grandes defectos relacionados con la toma de decisiones en medicina. el primer defecto eran las variaciones injustificadas en la práctica clínica entre los profesionales y el segundo, la brecha de tiempo entre la investigación científica y la práctica clínica. Estos defectos ponían en duda la calidad de las decisiones tomadas en medicina y así junto a la MBE surgió la evaluación de la calidad de la evidencia, en la que una revisión de ensayos clínicos aleatorios es una evidencia de nivel superior mientras que el consenso de expertos se considera el nivel de evidencia más bajo. Sin embargo, aunque las GPC mejoran la calidad de atención al paciente, los estudios informan de que su utilización no se realiza de forma efectiva en la práctica clínica habitual, no tienen en cuenta la incertidumbre de la decisión ni los riesgos y beneficios que conllevan. Para suplir estas carencias, en los últimos años se han desarrollado nuevos Sistemas de Ayuda a la Decisión (SAD), que en el caso del CDIS se centran en la predicción del riesgo de RL. Así el aumento de frecuencia del CDIS desde las campañas de detección precoz, las diferentes estrategias de tratamiento aceptadas, la elevada probabilidad de recidivar como CINV y los déficits en el proceso actual de toma de decisiones basado en las GPC, son las razones principales que nos han motivado en la creación de un SAD para el tratamiento del CDIS, que basado en la Teoría de la Decisión Bayesiana nos permita modelar la secuencia de decisiones, gestionar la incertidumbre, calcular los riesgos y utilidades de cada decisión y así optimizar y facilitar la decisión de tratamiento. El objetivo principal del estudio es crear un modelo que tenga en cuenta todas las alternativas de tratamiento aceptadas en las GPC para el CDIS y las posibles consecuencias derivadas de ellos basándonos en la Teoría de la Decisión Bayesiana, validándolo con la evidencia científica, como objetivos secundarios comprobar sus resultados coherentes con la práctica clínica y realizar su adaptación informática. Los centros implicados en la investigación de este estudio han sido el Instituto Valenciano de Oncología (IVO) y la Universidad Politécnica de Valencia y dentro de ella el Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA). Los investigadores de ambos centros han colaborado para la creación del modelo y su validación. Se ha realizado la adaptación informática del modelo por los ingenieros informáticos y se han comprobado los resultados coherentes con la práctica clínica habitual en las pacientes diagnosticadas de CDIS y tratadas en el IVO en los últimos 17 años.Breast carcinoma (BC) is the most common malignant tumor in women and the incidence increases by 2% annually, so it is one of the most important health problems in industrialized countries. The National Health Systems are focused on early diagnosis to minimize the fatal consequences of the disease, conducting regular mammograms in women between 40 and 70 years. The development of these screening programs have shown to advance the diagnosis of BM a median time of 1.7 years and reduce mortality by 30% in patients over 40 years, 30% of tumors are less than or equal to 1 cm, in these tumors, the probability of lymph node involvement is very low (10-18%). These results are due to that it has been shown that the survival in tumor depends BC limiting disease to the breast. But early diagnosis has dramatically increased diagnosis of DCIS (DCIS), whose growth is limited to the breast duct and what is considered an early stage in the BC, but includes great variability of lesions greater or lesser risk develop an invasive carcinoma (CINV). According to the OMS classification, DCIS is a noninvasive epithelial tumor. TNM classification system is the most widely used in clinical practice because of its therapeutic and prognostic implications. Within the TNM classification DCIS is considered Tis N0 M0 as pure DCIS no nodal or metastatic involvement. In cases of DCIS with microinfiltration, the possibility of involvement and lymph node is not null and can present a microscopic or macroscopic involvement (pN0 (i +), pN1mi) or (pN1-N3) respectively. DCIS has a Breast Cancer Specific Survival (BCSS) close to 100% at 10 years, local recurrence (LR) is used as a marker of failure in the treatment of patients. More than half of the LR are invasive, reducing significantly statistically BCSS. The results of different studies that support that conservative surgery (CS) of the breast is not associated with a worse prognosis than techniques more aggressive, are extrapolated to DCIS and so is the surgical technique most commonly used, although in recent years it has observed an increase in subcutaneous mastectomy and prophylactic in young patients. Selective Sentinel node biopsy (SSNB) is not justified routinely in DCIS, only use recommended in DCIS with Microinvasion, in DCIS measurable in the MX or MRI, and one that requires a mastectomy, despite these recommendations is performed in 15% of patients. The treatment is considered preventive treatment and is based in whole breast radiation therapy and hormone therapy, mainly with Tamoxifen (TAM) for 5 years. There prognostic factors that will determine the evolution of the CM, will depend both on the same aggressiveness and the ability of immune response of the patient. The most important prognostic factor is lymph node involvement significantly decreasing survival at 5 years. The expression of receptors have also been shown to be related to prognosis, positivity carries a favorable prognosis and justifies the hormonal treatment, so that its determination is mandatory in all cases and helps to classify the tumor in molecular phenotypes that have been shown to predict recurrence early.  Medical decisions are based on Clinical Practice Guidelines (CPG) providing us recommendations and medical practice guidelines that help us to optimal patient care. Their recommendations are based on Evidence Based Medicine (EBM), which began to avoid major defects related to decision making in medicine. the first defect was unjustified variations in clinical practice among professionals and the second, the time gap between scientific research and clinical practice. These defects questioned the quality of decisions made in medicine and so by the MBE evaluation of the quality of the evidence emerged. A review of randomized clinical trials is evidence of higher level while expert consensus It is considered the lowest level of evidence. However, although the GPC improve the quality of patient care, studies report that its use is not done effectively in routine clinical practice, they do not take into account the uncertainty of the decision or the risks and benefits involved. To fill these gaps, in recent years have developed new Systems Decision Support (SDS), which in the case of DCIS focus on risk prediction RL.  Thus increasing frequency of DCIS from campaigns early detection, different strategies accepted treatment, the high probability of relapse as CINV and deficits in the current process of decision making based on the GPC, are the main reasons we have led to the creation of a SDS for the treatment of DCIS, which based on the theory of Bayesian decision allows us to model the sequence of decisions, manage uncertainty, calculate the risks and profits of each decision and optimize and facilitate decision treatment. The main objective of the study is to create a model that takes into account all treatment alternatives accepted in the GPC for DCIS and the possible consequences of them based on the theory of Bayesian decision validating it with scientific evidence, as secondary objectives check your results consistent with clinical practice and make your computer adaptation.  The research centers involved in this study were the Valencian Institute of Oncology (IVO) and the Polytechnic University of Valencia and within the Institute for the Application of Information Technology and Advanced Communications (ITACA). Researchers at both centers have collaborated for model creation and validation. It has made the computer adaptation of the model by computer engineers and have found the results consistent with routine clinical practice in patients diagnosed with DCIS and treated in the IVO in the last 17 years