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INTRODUCTION: Chronic Lymphocytic Leukemia (CLL) is a rare hematologic malignancy to occur in pregnancy, with an estimated incidence of 1 in 75,000 pregnancies. OBJECTIVE: This review aimed to summarize the current evidence regarding the diagnosis, prognosis, and treatment of CLL in pregnant cases. METHODS: A comprehensive search strategy was employed across multiple databases, yielding 14 case reports for inclusion. The cases were divided based on CLL diagnosis onset, either before or during pregnancy. RESULTS: Patients diagnosed during pregnancy (n=5) were mostly asymptomatic at diagnosis, with management ranging from supportive care to leukapheresis and transfusions. Postpartum treatment varied, with some patients requiring no additional therapy and others receiving chemotherapy. Pregnancy outcomes were generally favorable, with most neonates born healthy at term. However, one case of Richter transformation resulted in maternal death despite treatment. Among patients with pre-existing CLL (n=9), the majority experienced an indolent course during pregnancy, with only supportive care required. A few cases necessitated treatment due to progressive disease or complications, including chemotherapy, leukapheresis, and splenectomy. CONCLUSION: Overall, this review highlights the heterogeneous nature of CLL in pregnancy and the importance of individualized management based on disease severity, gestational age, and maternal-fetal risks. Close monitoring, supportive care, and a multidisciplinary approach are essential for optimizing outcomes in this rare and complex clinical scenario.
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The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Neoplasias Mamarias Animales , Neoplasias de la Mama Triple Negativas , Humanos , Animales , Linfocitos Infiltrantes de Tumor , Biomarcadores , Aprendizaje AutomáticoRESUMEN
Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research. However, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedures and produces massive data that capture tumor histologic details at high resolution. Furthermore, the rapid development of deep learning algorithms has significantly increased the efficiency and accuracy of pathology image analysis. In light of this progress, digital pathology is fast becoming a powerful tool to assist pathologists. Studying tumor tissue and its surrounding microenvironment provides critical insight into tumor initiation, progression, metastasis, and potential therapeutic targets. Nucleus segmentation and classification are critical to pathology image analysis, especially in characterizing and quantifying the tumor microenvironment (TME). Computational algorithms have been developed for nucleus segmentation and TME quantification within image patches. However, existing algorithms are computationally intensive and time consuming for WSI analysis. This study presents Histology-based Detection using Yolo (HD-Yolo), a new method that significantly accelerates nucleus segmentation and TME quantification. We demonstrate that HD-Yolo outperforms existing WSI analysis methods in nucleus detection, classification accuracy, and computation time. We validated the advantages of the system on 3 different tissue types: lung cancer, liver cancer, and breast cancer. For breast cancer, nucleus features by HD-Yolo were more prognostically significant than both the estrogen receptor status by immunohistochemistry and the progesterone receptor status by immunohistochemistry. The WSI analysis pipeline and a real-time nucleus segmentation viewer are available at https://github.com/impromptuRong/hd_wsi.
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Neoplasias de la Mama , Aprendizaje Profundo , Humanos , Femenino , Microambiente Tumoral , Algoritmos , Procesamiento de Imagen Asistido por Computador/métodos , Neoplasias de la Mama/patologíaRESUMEN
MOTIVATION: Nucleus detection, segmentation and classification are fundamental to high-resolution mapping of the tumor microenvironment using whole-slide histopathology images. The growing interest in leveraging the power of deep learning to achieve state-of-the-art performance often comes at the cost of explainability, yet there is general consensus that explainability is critical for trustworthiness and widespread clinical adoption. Unfortunately, current explainability paradigms that rely on pixel saliency heatmaps or superpixel importance scores are not well-suited for nucleus classification. Techniques like Grad-CAM or LIME provide explanations that are indirect, qualitative and/or nonintuitive to pathologists. RESULTS: In this article, we present techniques to enable scalable nuclear detection, segmentation and explainable classification. First, we show how modifications to the widely used Mask R-CNN architecture, including decoupling the detection and classification tasks, improves accuracy and enables learning from hybrid annotation datasets like NuCLS, which contain mixtures of bounding boxes and segmentation boundaries. Second, we introduce an explainability method called Decision Tree Approximation of Learned Embeddings (DTALE), which provides explanations for classification model behavior globally, as well as for individual nuclear predictions. DTALE explanations are simple, quantitative, and can flexibly use any measurable morphological features that make sense to practicing pathologists, without sacrificing model accuracy. Together, these techniques present a step toward realizing the promise of computational pathology in computer-aided diagnosis and discovery of morphologic biomarkers. AVAILABILITY AND IMPLEMENTATION: Relevant code can be found at github.com/CancerDataScience/NuCLS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Núcleo Celular , Árboles de DecisiónRESUMEN
Whole slide imaging, which is an important technique in the field of digital pathology, has recently been the subject of increased interest and avenues for utilisation, and with more widespread whole slide image (WSI) utilisation, there will also be increased interest in and implementation of image analysis (IA) techniques. IA includes artificial intelligence (AI) and targeted or hypothesis-driven algorithms. In the overall pathology field, the number of citations related to these topics has increased in recent years. Renal pathology is one anatomical pathology subspecialty that has utilised WSIs and IA algorithms; it can be argued that renal transplant pathology could be particularly suited for whole slide imaging and IA, as renal transplant pathology is frequently classified by use of the semiquantitative Banff classification of renal allograft pathology. Hypothesis-driven/targeted algorithms have been used in the past for the assessment of a variety of features in the kidney (e.g. interstitial fibrosis, tubular atrophy, inflammation); in recent years, the amount of research has particularly increased in the area of AI/machine learning for the identification of glomeruli, for histological segmentation, and for other applications. Deep learning is the form of machine learning that is most often used for such AI approaches to the 'big data' of pathology WSIs, and deep learning methods such as artificial neural networks (ANNs)/convolutional neural networks (CNNs) are utilised. Unsupervised and supervised AI algorithms can be employed to accomplish image or semantic classification. In this review, AI and other IA algorithms applied to WSIs are discussed, and examples from renal pathology are covered, with an emphasis on renal transplant pathology.
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Aloinjertos/patología , Inteligencia Artificial , Trasplante de Riñón , Riñón/patología , Humanos , Procesamiento de Imagen Asistido por Computador , Enfermedades Renales/patología , Enfermedades Renales/cirugía , Aprendizaje AutomáticoRESUMEN
Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes. In this study, we show a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how these survival convolutional neural networks (SCNNs) can integrate information from both histology images and genomic biomarkers into a single unified framework to predict time-to-event outcomes and show prediction accuracy that surpasses the current clinical paradigm for predicting the overall survival of patients diagnosed with glioma. We use statistical sampling techniques to address challenges in learning survival from histology images, including tumor heterogeneity and the need for large training cohorts. We also provide insights into the prediction mechanisms of SCNNs, using heat map visualization to show that SCNNs recognize important structures, like microvascular proliferation, that are related to prognosis and that are used by pathologists in grading. These results highlight the emerging role of deep learning in precision medicine and suggest an expanding utility for computational analysis of histology in the future practice of pathology.
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Neoplasias Encefálicas/genética , Neoplasias Encefálicas/patología , Genómica/métodos , Glioma/genética , Glioma/patología , Técnicas Histológicas/métodos , Redes Neurales de la Computación , Algoritmos , Neoplasias Encefálicas/terapia , Glioma/terapia , Humanos , Procesamiento de Imagen Asistido por Computador , Medicina de Precisión , PronósticoRESUMEN
Bone marrow aspirate (BMA) differential cell counts (DCCs) are critical for the classification of hematologic disorders. While manual counts are considered the gold standard, they are labor intensive, time consuming, and subject to bias. A reliable automated counter has yet to be developed, largely due to the inherent complexity of bone marrow specimens. Digital pathology imaging coupled with machine learning algorithms represents a highly promising emerging technology for this purpose. Yet, training datasets for BMA cellular constituents, critical for building and validating machine learning algorithms, are lacking. Herein, we report our experience creating and employing such datasets to develop a machine learning algorithm to detect and classify BMA cells. Utilizing a web-based system that we developed for annotating and managing digital pathology images, over 10,000 cells from scanned whole slide images of BMA smears were manually annotated, including all classes that comprise the standard clinical DCC. We implemented a two-stage, detection and classification approach that allows design flexibility and improved classification accuracy. In a sixfold cross-validation, our algorithms achieved high overall accuracy in detection (0.959 ± 0.008 precision-recall AUC) and classification (0.982 ± 0.03 ROC AUC) using nonneoplastic samples. Testing on a small set of acute myeloid leukemia and multiple myeloma samples demonstrated similar detection and classification performance. In summary, our algorithms showed promising early results and represent an important initial step in the effort to devise a reliable, objective method to automate DCCs. With further development to include formal clinical validation, such a system has the potential to assist in disease diagnosis and prognosis, and significantly impact clinical practice.
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Células de la Médula Ósea , Aprendizaje Automático , Patología/métodos , Recuento de Células , Conjuntos de Datos como Asunto , HumanosRESUMEN
MOTIVATION: While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images. RESULTS: We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy. AVAILABILITY AND IMPLEMENTATION: Dataset is freely available at: https://goo.gl/cNM4EL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Neoplasias de la Mama , Colaboración de las Masas , Algoritmos , Técnicas Histológicas , HumanosRESUMEN
BACKGROUND: African American women experience a twofold higher incidence of triple-negative breast cancer (TNBC) and are 40% more likely to die from breast cancer than women of other ethnicities. However, the molecular bases for the survival disparity in breast cancer remain unclear, and no race-specific therapeutic targets have been proposed. To address this knowledge gap, we performed a systematic analysis of the relationship between gene mRNA expression and clinical outcomes determined for The Cancer Genome Atlas (TCGA) breast cancer patient cohort. METHODS: The systematic differential analysis of mRNA expression integrated with the analysis of clinical outcomes was performed for 1055 samples from the breast invasive carcinoma TCGA PanCancer cohorts. A deep learning fully-convolutional model was used to determine the association between gene expression and tumor features based on breast cancer patient histopathological images. RESULTS: We found that more than 30% of all protein-coding genes are differentially expressed in White and African American breast cancer patients. We have determined a set of 32 genes whose overexpression in African American patients strongly correlates with decreased survival of African American but not White breast cancer patients. Among those genes, the overexpression of mitogen-activated protein kinase kinase 3 (MKK3) has one of the most dramatic and race-specific negative impacts on the survival of African American patients, specifically with triple-negative breast cancer. We found that MKK3 can promote the TNBC tumorigenesis in African American patients in part by activating of the epithelial-to-mesenchymal transition induced by master regulator MYC. CONCLUSIONS: The poor clinical outcomes in African American women with breast cancer can be associated with the abnormal elevation of individual gene expression. Such genes, including those identified and prioritized in this study, could represent new targets for therapeutic intervention. A strong correlation between MKK3 overexpression, activation of its binding partner and major oncogene MYC, and worsened clinical outcomes suggests the MKK3-MYC protein-protein interaction as a new promising target to reduce racial disparity in breast cancer survival.
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Neoplasias de la Mama , Neoplasias de la Mama Triple Negativas , Negro o Afroamericano/genética , Neoplasias de la Mama/genética , Transformación Celular Neoplásica , Femenino , Humanos , Incidencia , Neoplasias de la Mama Triple Negativas/genética , Población Blanca/genéticaRESUMEN
BACKGROUND: One of the most notable recent advances in electron microscopy (EM) was the development of genetically-encoded EM tags, including the fluorescent flavoprotein Mini-SOG (Mini-Singlet Oxygen Generator). Mini-SOG generates good EM contrast, thus providing a viable alternative to technically-demanding methods such as immuno-electron microcopy (immuno-EM). Based on the Mini-SOG technology, in this paper, we describe the construction, validation and optimization of a series of vectors which allow expression of Mini-SOG in the Drosophila melanogaster genetic model system. FINDINGS: We constructed a Mini-SOG tag that has been codon-optimized for expression in Drosophila (DMS tag) using PCR-mediated gene assembly. The photo-oxidation reaction triggered by DMS was then tested using these vectors in Drosophila cell lines. DMS tag did not affect the subcellular localization of the proteins we tested. More importantly, we demonstrated the utility of the DMS tag for EM in Drosophila by showing that it can produce robust photo-oxidation reactions in the presence of blue light and the substrate DAB; the resultant electron micrographs contain electron-dense regions corresponding to the protein of interest. The vectors we generated allow protein tagging at both termini, for constitutive and inducible protein expression, as well as the generation of transgenic lines by P-element transformation. CONCLUSIONS: We demonstrated the feasibility of Mini-SOG tagging in Drosophila. The constructed vectors will no doubt be a useful molecular tool for genetic tagging to facilitate high-resolution localization of proteins in Drosophila by electron microscopy.
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The use of Web 2.0 tools in education and health care has received heavy attention over the past years. Over two consecutive years, Children's Cancer Hospital - Egypt 57357 (CCHE 57357), in collaboration with Egyptian universities, student bodies, and NGOs, conducted a summer course that supports undergraduate medical students to cross the gap between clinical practice and clinical research. This time, there was a greater emphasis on reaching out to the students using social media and other Web 2.0 tools, which were heavily used in the course, including Google Drive, Facebook, Twitter, YouTube, Mendeley, Google Hangout, Live Streaming, Research Electronic Data Capture (REDCap), and Dropbox. We wanted to investigate the usefulness of integrating Web 2.0 technologies into formal educational courses and modules. The evaluation survey was filled in by 156 respondents, 134 of whom were course candidates (response rate = 94.4 %) and 22 of whom were course coordinators (response rate = 81.5 %). The course participants came from 14 different universities throughout Egypt. Students' feedback was positive and supported the integration of Web 2.0 tools in academic courses and modules. Google Drive, Facebook, and Dropbox were found to be most useful.
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Investigación Biomédica/educación , Instrucción por Computador/métodos , Educación de Pregrado en Medicina/métodos , Medios de Comunicación Sociales , Estudiantes de Medicina/estadística & datos numéricos , Centros Médicos Académicos , Países en Desarrollo , Egipto , Humanos , IndustriasRESUMEN
Tumor-Infiltrating Lymphocytes (TILs) have strong prognostic and predictive value in breast cancer, but their visual assessment is subjective. To improve reproducibility, the International Immuno-oncology Working Group recently released recommendations for the computational assessment of TILs that build on visual scoring guidelines. However, existing resources do not adequately address these recommendations due to the lack of annotation datasets that enable joint, panoptic segmentation of tissue regions and cells. Moreover, existing deep-learning methods focus entirely on either tissue segmentation or cell nuclei detection, which complicates the process of TILs assessment by necessitating the use of multiple models and reconciling inconsistent predictions. We introduce PanopTILs, a region and cell-level annotation dataset containing 814,886 nuclei from 151 patients, openly accessible at: sites.google.com/view/panoptils . Using PanopTILs we developed MuTILs, a neural network optimized for assessing TILs in accordance with clinical recommendations. MuTILs is a concept bottleneck model designed to be interpretable and to encourage sensible predictions at multiple resolutions. Using a rigorous internal-external cross-validation procedure, MuTILs achieves an AUROC of 0.93 for lymphocyte detection and a DICE coefficient of 0.81 for tumor-associated stroma segmentation. Our computational score closely matched visual scores from 2 pathologists (Spearman R = 0.58-0.61, p < 0.001). Moreover, computational TILs scores had a higher prognostic value than visual scores, independent of TNM stage and patient age. In conclusion, we introduce a comprehensive open data resource and a modeling approach for detailed mapping of the breast tumor microenvironment.
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Breast cancer is a heterogeneous disease with variable survival outcomes. Pathologists grade the microscopic appearance of breast tissue using the Nottingham criteria, which are qualitative and do not account for noncancerous elements within the tumor microenvironment. Here we present the Histomic Prognostic Signature (HiPS), a comprehensive, interpretable scoring of the survival risk incurred by breast tumor microenvironment morphology. HiPS uses deep learning to accurately map cellular and tissue structures to measure epithelial, stromal, immune, and spatial interaction features. It was developed using a population-level cohort from the Cancer Prevention Study-II and validated using data from three independent cohorts, including the Prostate, Lung, Colorectal, and Ovarian Cancer trial, Cancer Prevention Study-3, and The Cancer Genome Atlas. HiPS consistently outperformed pathologists in predicting survival outcomes, independent of tumor-node-metastasis stage and pertinent variables. This was largely driven by stromal and immune features. In conclusion, HiPS is a robustly validated biomarker to support pathologists and improve patient prognosis.
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Neoplasias de la Mama , Femenino , Humanos , Neoplasias de la Mama/diagnóstico por imagen , Neoplasias de la Mama/patología , Ensayos Clínicos como Asunto , Microambiente Tumoral/genética , Procesamiento de Imagen Asistido por Computador , Aprendizaje ProfundoRESUMEN
BACKGROUNDS & AIM: Placental insufficiency is a serious complication that affects pregnancy and fetal growth. Cyclophosphamide (CYC) is considered one of the chemotherapeutic agents. Unfortunately, CYC not only affects tumor cells but also affects healthy cells causing multiple injuries including the placenta. The present study aimed to evaluate the effect of cysteinyl leukotriene receptor antagonist; montelukast (MK), on CYC-induced placental injury in rats. MATERIALS AND METHODS: Forty-eight female Wister rats were randomly divided into 8 experimental groups. Group 1: control pregnant group; Group 2: MK 5 mg-treated pregnant rats; Group 3: MK 10 mg-treated pregnant rats; Group 4: MK 20 mg-treated pregnant rats; Group 5: pregnant rats received CYC (20 mg/kg, i.p); Group 6: pregnant rats received MK 5 mg and CYC; Group 7: pregnant rats received MK 10 mg and CYC; Group 8: pregnant rats received MK 20 mg and CYC. Placental malondialdehyde (MDA), reduced glutathione (GSH), total antioxidant capacity (TAC), placental growth factor (PlGF), and Nod-like receptor p3 (NLRP3) inflammasome were measured. Histological changes, interleukin-1ß (IL-1ß), and cleaved caspase-3 immuno-expressions were also evaluated. RESULTS: CYC showed a significant decrease in placental GSH, TAC, and PlGF with a significant increase in placental MDA, NLRP3, and immuno-expression of IL-1ß and caspase-3. MK showed significant improvement in all oxidative stress (MDA, GSH and TAC), inflammatory (NLRP3 and IL-1ß), and apoptotic (caspase-3) parameters. CONCLUSION: According to the findings, MK was proved to have a possible protective role in CYC-induced placental injury via modulation of NLRP3/IL-1ß signaling pathway with anti-oxidant, anti-inflammatory, and anti-apoptotic effects.
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Acetatos , Ciclofosfamida , Ciclopropanos , Interleucina-1beta , Antagonistas de Leucotrieno , Proteína con Dominio Pirina 3 de la Familia NLR , Placenta , Quinolinas , Ratas Wistar , Transducción de Señal , Sulfuros , Animales , Femenino , Embarazo , Proteína con Dominio Pirina 3 de la Familia NLR/metabolismo , Proteína con Dominio Pirina 3 de la Familia NLR/antagonistas & inhibidores , Ciclofosfamida/toxicidad , Ciclofosfamida/efectos adversos , Quinolinas/farmacología , Quinolinas/uso terapéutico , Acetatos/uso terapéutico , Acetatos/farmacología , Interleucina-1beta/metabolismo , Placenta/efectos de los fármacos , Placenta/patología , Placenta/metabolismo , Antagonistas de Leucotrieno/farmacología , Antagonistas de Leucotrieno/uso terapéutico , Transducción de Señal/efectos de los fármacos , Ratas , Factor de Crecimiento Placentario/metabolismo , Estrés Oxidativo/efectos de los fármacos , Inflamasomas/metabolismo , Apoptosis/efectos de los fármacosRESUMEN
Breast cancer is a heterogeneous disease with variable survival outcomes. Pathologists grade the microscopic appearance of breast tissue using the Nottingham criteria, which is qualitative and does not account for non-cancerous elements within the tumor microenvironment (TME). We present the Histomic Prognostic Signature (HiPS), a comprehensive, interpretable scoring of the survival risk incurred by breast TME morphology. HiPS uses deep learning to accurately map cellular and tissue structures in order to measure epithelial, stromal, immune, and spatial interaction features. It was developed using a population-level cohort from the Cancer Prevention Study (CPS)-II and validated using data from three independent cohorts, including the PLCO trial, CPS-3, and The Cancer Genome Atlas. HiPS consistently outperformed pathologists' performance in predicting survival outcomes, independent of TNM stage and pertinent variables. This was largely driven by stromal and immune features. In conclusion, HiPS is a robustly validated biomarker to support pathologists and improve prognosis.
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The accurate diagnosis of small-cell lung cancer (SCLC) is crucial, as treatment strategies differ from those of other lung cancers. This systematic review aims to identify proteins differentially expressed in SCLC compared to normal lung tissue, evaluating their potential utility in diagnosing and prognosing the disease. Additionally, the study identifies proteins differentially expressed between SCLC and large cell neuroendocrine carcinoma (LCNEC), aiming to discover biomarkers distinguishing between these two subtypes of neuroendocrine lung cancers. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted across PubMed/MEDLINE, Scopus, Embase, and Web of Science databases. Studies reporting proteomics information and confirming SCLC and/or LCNEC through histopathological and/or cytopathological examination were included, while review articles, non-original articles, and studies based on animal samples or cell lines were excluded. The initial search yielded 1705 articles, and after deduplication and screening, 16 articles were deemed eligible. These studies revealed 117 unique proteins significantly differentially expressed in SCLC compared to normal lung tissue, along with 37 unique proteins differentially expressed between SCLC and LCNEC. In conclusion, this review highlights the potential of proteomics technology in identifying novel biomarkers for diagnosing SCLC, predicting its prognosis, and distinguishing it from LCNEC.
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Chronic lymphocytic leukemia (CLL) is a B cell neoplasm characterized by the accumulation of aberrant monoclonal B lymphocytes. CLL is the predominant type of leukemia in Western countries, accounting for 25% of cases. Although many patients remain asymptomatic, a subset may exhibit typical lymphoma symptoms, acquired immunodeficiency disorders, or autoimmune complications. Diagnosis involves blood tests showing increased lymphocytes and further examination using peripheral blood smear and flow cytometry to confirm the disease. With the significant advancements in machine learning (ML) and artificial intelligence (AI) in recent years, numerous models and algorithms have been proposed to support the diagnosis and classification of CLL. In this review, we discuss the benefits and drawbacks of recent applications of ML algorithms in the diagnosis and evaluation of patients diagnosed with CLL.
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Leucemia Linfocítica Crónica de Células B , Linfoma , Humanos , Leucemia Linfocítica Crónica de Células B/diagnóstico , Leucemia Linfocítica Crónica de Células B/patología , Inteligencia Artificial , Linfocitos B/patología , Linfoma/patología , Aprendizaje AutomáticoRESUMEN
Purpose: Short sleep duration and quality are increasingly common in the Middle East and North Africa (MENA) region and has been linked to metabolic syndrome, which increases the risk of cardiovascular disease and diabetes. This study aimed to examine the link between short sleep duration and metabolic syndrome. Patients and Methods: We conducted a case-control study using data from Qatar Biobank, with 1000 participants categorized into two groups: less than 7 hours of sleep (n=500) and 7 or more hours of sleep (n=500). Metabolic syndrome was defined using WHO criteria, and logistic regression analysis adjusted for age and gender. Results: There was a higher proportion of individuals with MetS in the short sleep duration group compared to the normal sleep duration group (22.8% vs 15.8%, respectively). The multivariable regression showed that short sleep duration was associated with metabolic syndrome (OR 1.91, 95% CI: 1.14-3.20, P=0.014) and having 1-2 components of metabolic syndrome (OR 1.91, 95% CI: 1.14-3.20, P=0.014), particularly in males (OR: 2.30, 95% CI: 1.07-4.94, P=0.032). Being overweight (OR 2.17, 95% CI: 1.30-3.63, P=0.003) was also associated with a shorter sleep duration. BMI was identified as the main contributor to the association between short sleep duration and metabolic syndrome, while diabetes played a minor role. Conclusion: Short sleep duration was associated with metabolic syndrome in Qatar, particularly in males.
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Artificial intelligence (AI) is rapidly becoming an established arm in medical sciences and clinical practice in numerous medical fields. Its implications have been rising and are being widely used in research, diagnostics, and treatment options for many pathologies, including sickle cell disease (SCD). AI has started new ways to improve risk stratification and diagnosing SCD complications early, allowing rapid intervention and reallocation of resources to high-risk patients. We reviewed the literature for established and new AI applications that may enhance management of SCD through advancements in diagnosing SCD and its complications, risk stratification, and the effect of AI in establishing an individualized approach in managing SCD patients in the future. Aim: to review the benefits and drawbacks of resources utilizing AI in clinical practice for improving the management for SCD cases.
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Myelodysplastic syndrome (MDS) is composed of diverse hematological malignancies caused by dysfunctional stem cells, leading to abnormal hematopoiesis and cytopenia. Approximately 30% of MDS cases progress to acute myeloid leukemia (AML), a more aggressive disease. Early detection is crucial to intervene before MDS progresses to AML. The current diagnostic process for MDS involves analyzing peripheral blood smear (PBS), bone marrow sample (BMS), and flow cytometry (FC) data, along with clinical patient information, which is labor-intensive and time-consuming. Recent advancements in machine learning offer an opportunity for faster, automated, and accurate diagnosis of MDS. In this review, we aim to provide an overview of the current applications of AI in the diagnosis of MDS and highlight their advantages, disadvantages, and performance metrics.