Modified osprey algorithm for optimizing capsule neural network in leukemia image recognition.

The diagnosis of leukemia is a serious matter that requires immediate and accurate attention. This research presents a revolutionary method for diagnosing leukemia using a Capsule Neural Network (CapsNet) with an optimized design. CapsNet is a cutting-edge neural network that effectively captures complex features and spatial relationships within images. To improve the CapsNet's performance, a Modified Version of Osprey Optimization Algorithm (MOA) has been utilized. Thesuggested approach has been tested on the ALL-IDB database, a widely recognized dataset for leukemia image classification. Comparative analysis with various machine learning techniques, including Combined combine MobilenetV2 and ResNet18 (MBV2/Res) network, Depth-wise convolution model, a hybrid model that combines a genetic algorithm with ResNet-50V2 (ResNet/GA), and SVM/JAYA demonstrated the superiority of our method in different terms. As a result, the proposed method is a robust and powerful tool for diagnosing leukemia from medical images.

Imaging findings of breast leukaemia: a case series.

Aims/Background Breast leukaemia (BL) is a rare breast malignancy that is treated differently from other malignant conditions. However, it is easily confused with other conditions; therefore, how to accurately diagnose is crucial. We retrospectively analysed the imaging findings of 13 patients to provide a diagnostic reference. Methods From January 2015 to April 2023, 13 patients with BL confirmed by biopsy who underwent imaging in Peking University People's hospital were retrospectively analysed. The imaging findings obtained via ultrasound (US), mammography (MMG), magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT) were analysed, and the detection rates of these methods for diagnosing BL were compared. Results Twenty-nine lesions were detected in the 13 patients. These patients presented with palpable masses or breast swelling several months after treatment for leukaemia, mainly involving the bilateral breasts. Ultrasonography was performed for 13 patients, and all lesions were detected. Most of the identified masses were hypoechoic and had indistinct boundaries, irregular shapes, no enhancement of the posterior echo, and no abundant blood flow. MMG was performed for five patients, revealing breast masses, architectural distortion, and no abnormalities. MRI was performed for four patients, and all lesions were detected; most of the lesions were hypointense on T1-weighted imaging and hyperintense on T2-weighted imaging and diffusion-weighted imaging, with a decreased apparent diffusion coefficient and inhomogeneous enhancement. The enhancement curves were mostly inflow patterns. PET/CT was performed for four patients; two patients had hypermetabolism, and the other two had no obvious radioactive uptake. Conclusion Compared to MMG and PET/CT, US and MRI have higher detection rates. Furthermore, compared to MRI, US is inexpensive, convenient and efficient; therefore, it should be the first choice for diagnosing BL.

Machine learning-based model for predicting outcomes in cerebral hemorrhage patients with leukemia.

BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) in leukemia patients progresses rapidly with high mortality. Limited data are available on imaging studies in this population. The study aims to develop prediction models for 7-day and short-term mortality risk based on the non-contrast computed tomography (NCCT) image features. METHODS: The NCCT image features of ICH in 135 leukemia patients between 2007-2023 were retrospectively extracted using manual assessment and radiomics methods. After multiple imputation of missing laboratory data, univariate logistic regression and least absolute shrinkage and selection operator (LASSO) were used for feature selection. Random forest models were built with comprehensive evaluation and ranking of feature importance. RESULT: 135 and 129 patients were included in the studies for 7-day and short-term prognostic models, respectively. The median age of all enrolled patients was 35 years, and there were 86 male patients (63.7 %). Clinical models (validation: AUC [area under the curve] = 0.78, AUPRC [area under the precision-recall curve] = 0.73; AUC = 0.84, AUPRC = 0.86), radiomics models (validation: AUC = 0.82, AUPRC = 0.78; AUC = 0.75, AUPRC = 0.77), and the combined models (validation: AUC = 0.84, AUPRC = 0.83; AUC = 0.87, AUPRC = 0.89) predicted 7-day and short-term mortality with good predictive efficacy. Clinical decision curve analysis showed that the combined models predicted 7-day and 30-day risk of death would be more beneficial than other models. Shape features contributed significantly more than semantic features in both radiomics models and combined models (93.3 %, 52.1 %, as well as 85.2 %,37.4 %, respectively) for 7-day and 30-day mortality. CONCLUSIONS: Combined models constructed based on NCCT perform well in predicting the risk of 7-day and short-term mortality in ICH patients with leukemia. Shape features extracted by radiomics are important markers for modeling the prognosis.

PET/CT in leukemia: utility and future directions.

2-Deoxy-2-[ 18 F]fluoro- d -glucose PET/computed tomography ([ 18 F]FDG PET/CT) has proven to be a sensitive method for the detection and evaluation of hematologic malignancies, especially lymphoma. The increasing incidence and mortality rates of leukemia have raised significant concerns. Through the utilization of whole-body imaging, [ 18 F]FDG PET/CT provides a thorough assessment of the entire bone marrow, complementing the limited insights provided by biopsy samples. In this regard, [ 18 F]FDG PET/CT has the ability to assess diverse types of leukemia The utilization of [ 18 F]FDG PET/CT has been found to be effective in evaluating leukemia spread beyond the bone marrow, tracking disease relapse, identifying Richter's transformation, and assessing the inflammatory activity associated with acute graft versus host disease. However, its role in various clinical scenarios in leukemia remains unacknowledged. Despite their less common use, some novel PET/CT radiotracers are being researched for potential use in specific scenarios in leukemia patients. Therefore, the objectives of this review are to provide a thorough assessment of the current applications of [ 18 F]FDG PET/CT in the staging and monitoring of leukemia patients, as well as the potential for an expanding role of PET/CT in leukemia patients.

Sociodemographic factors and Child Opportunity Index disparities associated with missed care opportunities in pediatric patients with lymphoma and leukemia referred for FDG-PET/CT.

BACKGROUND: Little data exists on the association of missed care opportunities (MCOs) in children referred for nuclear medicine/nuclear oncology imaging examinations and socioeconomic disparities. OBJECTIVE: To determine the prevalence of MCOs in children with lymphoma/leukemia scheduled for fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) and the impact of sociodemographic factors and Child Opportunity Index (COI). MATERIALS AND METHODS: Retrospective analysis of MCOs in children with lymphoma/leukemia scheduled for FDG-PET/CT (2012 to 2022) was performed. In univariate analysis, patient, neighborhood, and appointment data were assessed across MCOs and completed appointments. Logistic regression evaluated independent effects of patient-, neighborhood-, and appointment-level factors with MCOs. Two-sided P-value < .05 was considered statistically significant. RESULTS: In 643 FDG-PET/CT appointments (n = 293 patients; median age 15 years (IQR 11.0-17.0 years); 37.9% female), there were 20 MCOs (3.1%) involving 16 patients. Only 8.2% appointments involved Black/African American non-Hispanic/Latino patients, yet they made up a quarter of total MCOs. Patients living in neighborhoods with very low or low COI experienced significantly higher MCOs versus zip codes with very high COI (6.9% vs. 0.8%; P = 0.02). Logistic regression revealed significantly increased likelihood of MCOs for patients aged 18 to 21 [odds ratio (OR) 4.50; 95% CI 1.53-13.27; P = 0.007], Black/African American non-Hispanic/Latino (OR 3.20; 95% CI 1.08-9.49; P = 0.04), zip codes with very low or low COI (OR 9.60; 95% CI 1.24-74.30; P = 0.03), and unknown insurance status. CONCLUSION: Children with lymphoma/leukemia, living in zip codes with very low or low COI, and who identified as Black/African American non-Hispanic/Latino experienced more MCOs. Our study supports the need to address intersecting sociodemographic, neighborhood, and health system factors that will improve equitable access to necessary healthcare imaging for children.

Morphological diagnosis of hematologic malignancy using feature fusion-based deep convolutional neural network.

Leukemia is a cancer of white blood cells characterized by immature lymphocytes. Due to blood cancer, many people die every year. Hence, the early detection of these blast cells is necessary for avoiding blood cancer. A novel deep convolutional neural network (CNN) 3SNet that has depth-wise convolution blocks to reduce the computation costs has been developed to aid the diagnosis of leukemia cells. The proposed method includes three inputs to the deep CNN model. These inputs are grayscale and their corresponding histogram of gradient (HOG) and local binary pattern (LBP) images. The HOG image finds the local shape, and the LBP image describes the leukaemia cell's texture pattern. The suggested model was trained and tested with images from the AML-Cytomorphology_LMU dataset. The mean average precision (MAP) for the cell with less than 100 images in the dataset was 84%, whereas for cells with more than 100 images in the dataset was 93.83%. In addition, the ROC curve area for these cells is more than 98%. This confirmed proposed model could be an adjunct tool to provide a second opinion to a doctor.

Clinical and ultrasonographic findings associated with testicular infiltration in pediatric patients with leukemia.

BACKGROUND: Testicular infiltration is infrequent in pediatric patients with leukemia and can be confused with other testicular conditions. OBJECTIVE: To analyze the presence of clinical and radiological features suggestive of testicular disease and its histological association with leukemia infiltration. METHOD: Retrospective and analytical observational study that included patients with diagnosis of leukemia who underwent biopsy for suspected testicular infiltration. The relationship with the variables analyzed were diagnosis, reason for taking the biopsy, ultrasound findings, stage of treatment, induration, increased volume and pain, with testicular infiltration. RESULTS: Eighteen patients were included; 11 of them with microlithiasis, of which one 1 reported infiltration (odds ratio: 0.075; p = 0.026), no association was found between ultrasound findings and the presence of infiltration. Clinical findings were significantly associated with positive biopsies. CONCLUSIONS: No risk association was found with the ultrasound findings such as microlithiasis and hypoechoic imaging. The clinically evident testicular disease (testicular enlargement and testicular induration) has a significant statistic association with the presence of leukemia infiltration.

ANTECEDENTES: La infiltración testicular en pacientes pediátricos con leucemia es infrecuente y puede ser confundida con otros padecimientos testiculares. OBJETIVO: Analizar la presencia de características clínicas y radiológicas sugestivas de enfermedad testicular y su asociación histológica con infiltración por leucemia. MÉTODO: Estudio observacional retrospectivo y analítico que incluyó a los pacientes con diagnóstico de leucemia sometidos a biopsia por sospecha de infiltración testicular. Se analizó la relación con las variables diagnóstico de base, motivo de toma de biopsia, hallazgos ultrasonográficos, etapa del tratamiento, induración, aumento de volumen y dolor, con infiltración a testículo. RESULTADOS: Se incluyeron 18 pacientes; de ellos, 11 con microlitiasis, de los cuales solo uno reportado con infiltración (odds ratio: 0.075; p = 0.026). No se encontró una asociación entre los hallazgos ultrasonográficos y la presencia de infiltración. Los hallazgos clínicos se asociaron significativamente con biopsias positivas. CONCLUSIONES: No se encontró una asociación de riesgo con los hallazgos por ultrasonido, como microlitiasis e imágenes hipoecogénicas. La enfermedad testicular clínicamente evidente (incremento de volumen e induración testicular) tiene una asociación estadísticamente significativa con la presencia de infiltración por leucemia.

MRI manifestations of central nervous system leukaemia and cytological analysis of the cerebrospinal fluid.

AIM: To investigate the magnetic resonance imaging (MRI) features and explore the value of MRI in the diagnosis of central nervous system leukaemia (CNSL). MATERIALS AND METHODS: A retrospective study was performed in 68 patients with leukaemia who underwent cranial MRI between January 2020 and June 2022 at Institute of Hematology and Blood Diseases Hospital. RESULTS: A total of 33 patients fulfilled the requirements for inclusion. The findings showed that 87.9% patients exhibited neurological symptoms, and 23 patients showed abnormal MRI findings. No differences were observed between the MRI+ and MRI- groups in terms of age, sex, neurological symptoms, glucose in the cerebrospinal fluid (CSF), chloride in the CSF, abnormal cells detected using conventional cytology (CC), bone marrow status at the diagnosis of CNSL, signal intensity ratio, and mortality, except for protein concentration and the number of leukaemic cells detected using flow cytometry (FCM) in the CSF. Kaplan-Meier survival analysis in patients with leukaemia revealed no statistical differences in the median survival times between the MRI+ group and MRI- group. Cox regression analysis and multivariate analysis showed no significant difference in survival rate between the MRI+ and MRI- groups. Kappa consistency test shows weak diagnostic consistency between MRI and CC, and weak diagnostic inconsistency between MRI and FCM. CONCLUSION: MRI could serve as an important complementary tool to CC and FCM in the diagnosis of CNSL, especially in patients without leptomeningeal involvement.

Childhood Leukemia Classification via Information Bottleneck Enhanced Hierarchical Multi-Instance Learning.

Leukemia classification relies on a detailed cytomorphological examination of Bone Marrow (BM) smear. However, applying existing deep-learning methods to it is facing two significant limitations. Firstly, these methods require large-scale datasets with expert annotations at the cell level for good results and typically suffer from poor generalization. Secondly, they simply treat the BM cytomorphological examination as a multi-class cell classification task, thus failing to exploit the correlation among leukemia subtypes over different hierarchies. Therefore, BM cytomorphological estimation as a time-consuming and repetitive process still needs to be done manually by experienced cytologists. Recently, Multi-Instance Learning (MIL) has achieved much progress in data-efficient medical image processing, which only requires patient-level labels (which can be extracted from the clinical reports). In this paper, we propose a hierarchical MIL framework and equip it with Information Bottleneck (IB) to tackle the above limitations. First, to handle the patient-level label, our hierarchical MIL framework uses attention-based learning to identify cells with high diagnostic values for leukemia classification in different hierarchies. Then, following the information bottleneck principle, we propose a hierarchical IB to constrain and refine the representations of different hierarchies for better accuracy and generalization. By applying our framework to a large-scale childhood acute leukemia dataset with corresponding BM smear images and clinical reports, we show that it can identify diagnostic-related cells without the need for cell-level annotations and outperforms other comparison methods. Furthermore, the evaluation conducted on an independent test cohort demonstrates the high generalizability of our framework.

Value of diffusion-weighted imaging in diagnosis and therapy response assessment of hepatic fungal infection in patients with acute leukemia.

OBJECTIVE: To investigate the role of diffusion-weighted imaging (DWI) for diagnosis and posttreatment assessment of hepatic fungal infection in patients with acute leukemia. METHODS: Patients with acute leukemia and highly suspected hepatic fungal infection were collected in the study. All the patients underwent MRI examination, including initial and follow-up DWI. The apparent diffusion coefficient (ADC) values of the lesions and the normal liver parenchyma were compared using Student's t-test. The ADC values of the hepatic fungal lesions of pretreatment and posttreatment were compared using paired t-test. RESULTS: A total of 13 patients with hepatic fungal infections have enrolled this study. Hepatic lesions were rounded or oval shaped, measured from 0.3 to 3 cm in diameter. The lesions showed significantly hyperintense signal on DWI and markedly hypointense signal on the ADC map, reflecting a marked restricted diffusion. The mean ADC values of the lesions were significantly lower than those of normal liver parenchyma (1.08 ± 0.34 × 10-3 vs. 1.98 ± 0.12 × 10-3 mm2 /s, p < 0.001). After treatment, the mean ADC values of the lesions were significantly increased when comparing with those of pretreatment (1.39 ± 0.29 × 10-3 vs. 1.06 ± 0.10 × 10-3 mm2 /s, p = .016). CONCLUSION: DWI can provide diffusion information of hepatic fungal infection in patients with acute leukemia, which could be taken as a valuable tool for diagnosis and therapy response assessment of these patients.

A Comparison of Convolutional Neural Networks and Traditional Feature-Based Classification Applied to Leukaemia Image Analysis.

The accuracy of smear test image classification is a fundamental aspect in differentiating the type of leukaemia and determining the right treatment to improve the patient's chances of survival and recovery. Image Classification has lately become a very effective tool in detecting and analysing the right type of leukaemia as each type of the disease looks differently when evaluated under microscope. This paper is evaluating and comparing the efficiency and performance of feature extraction techniques (colour descriptors and Haralick texture descriptors) and a CNN (Convolutional Neural Network) built and trained by using the TensorFlow packages for classifying leukaemia images. Extracting texture and colour features from a given set of leukaemia images through computation was successful in detecting the type of disease and the results analysed with Weka Classifiers were giving the highest accuracy of 93.58%. TensorFlow tested with Cross-Validation proves efficient in training and customising the system, but the accuracy was median 56% and was not greatly improved by addressing the class imbalance issue from the data set with SMOTE. Further studies will investigate increasing the number of images by using a segmentation and image manipulation/augmentation techniques and increasing the accuracy of CNN through the addition of the investigated traditional features.

Manipulation and Mechanical Deformation of Leukemia Cells by High-Frequency Ultrasound Single Beam.

Ultrasound single-beam acoustic tweezer system has attracted increasing attention in the field of biomechanics. Cell biomechanics play a pivotal role in leukemia cell functions. To better understand and compare the cell mechanics of the leukemia cells, herein, we fabricated an acoustic tweezer system in-house connected with a 50-MHz high-frequency cylinder ultrasound transducer. Selected leukemia cells (Jurkat, K562, and MV-411 cells) were cultured, trapped, and manipulated by high-frequency ultrasound single beam, which was transmitted from the ultrasound transducer without contacting any cells. The relative deformability of each leukemia cell was measured, characterized, and compared, and the leukemia cell (Jurkat cell) gaining the highest deformability was highlighted. Our results demonstrate that the high-frequency ultrasound single beam can be utilized to manipulate and characterize leukemia cells, which can be applied to study potential mechanisms in the immune system and cell biomechanics in other cell types.

Leukemia classification using the deep learning method of CNN.

BACKGROUND: Processing Low-Intensity Medical Images (LI-MI) is difficult as outcomes are varied when it comes to manual examination, which is also a time-consuming process. OBJECTIVE: To improve the quality of low-intensity images and identify the leukemia classification by utilizing CNN-based Deep Learning (DCNN) strategy. METHODS: The strategies employed for the recognition of leukemia classifications in the advised strategy are DCNN (ResNet-34 & DenseNet-121). The histogram equalization-based adaptive gamma correction followed by guided filtering applies to study the improvement in intensity and preserve the essential details of the image. The DCNN is used as a feature extractor to help classify leukemia types. Two datasets of ASH with 520 images and ALL-IDB with 559 images are used in this study. In 1,079 images, 779 are positive cases depicting leukemia and 300 images are negative (normal) cases. Thus, to validate performance of this DCNN strategy, ASH and ALL-IDB datasets are promoted in the investigation process to classify between positive and negative images. RESULTS: The DCNN classifier yieldes the overall classification accuracy of 99.2% and 98.4%, respectively. In addition, the achieved classification specificity, sensitivity, and precision are 99.3%, 98.7%, 98.4%, and 98.9%, 98.4%,98.6% applying to two datasets, respectively, which are higher than the performance using other machine learning classifiers including support vector machine, decision tree, naive bayes, random forest and VGG-16. CONCLUSION: Ths study demonstrates that the proposed DCNN enables to improve low-intensity images and accuracry of leukemia classification, which is superior to many of other machine leaning classifiers used in this research field.

Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia.

The diagnosis of leukemia involves the detection of the abnormal characteristics of blood cells by a trained pathologist. Currently, this is done manually by observing the morphological characteristics of white blood cells in the microscopic images. Though there are some equipment- based and chemical-based tests available, the use and adaptation of the automated computer vision-based system is still an issue. There are certain software frameworks available in the literature; however, they are still not being adopted commercially. So there is a need for an automated and software- based framework for the detection of leukemia. In software-based detection, segmentation is the first critical stage that outputs the region of interest for further accurate diagnosis. Therefore, this paper explores an efficient and hybrid segmentation that proposes a more efficient and effective system for leukemia diagnosis. A very popular publicly available database, the acute lymphoblastic leukemia image database (ALL-IDB), is used in this research. First, the images are pre-processed and segmentation is done using Multilevel thresholding with Otsu and Kapur methods. To further optimize the segmentation performance, the Learning enthusiasm-based teaching-learning-based optimization (LebTLBO) algorithm is employed. Different metrics are used for measuring the system performance. A comparative analysis of the proposed methodology is done with existing benchmarks methods. The proposed approach has proven to be better than earlier techniques with measuring parameters of PSNR and Similarity index. The result shows a significant improvement in the performance measures with optimizing threshold algorithms and the LebTLBO technique.

Deep learning-enabled whole slide imaging (DeepWSI): oil-immersion quality using dry objectives, longer depth of field, higher system throughput, and better functionality.

Whole slide imaging (WSI) has moved the traditional manual slide inspection process to the era of digital pathology. A typical WSI system translates the sample to different positions and captures images using a high numerical aperture (NA) objective lens. Performing oil-immersion microscopy is a major obstacle for WSI as it requires careful liquid handling during the scanning process. Switching between dry objective and oil-immersion lens is often impossible as it disrupts the acquisition process. For a high-NA objective lens, the sub-micron depth of field also poses a challenge to acquiring in-focus images of samples with uneven topography. Additionally, it implies a small field of view for each tile, thus limiting the system throughput and resulting in a long acquisition time. Here we report a deep learning-enabled WSI platform, termed DeepWSI, to substantially improve the system performance and imaging throughput. With this platform, we show that images captured with a regular dry objective lens can be transformed into images comparable to that of a 1.4-NA oil immersion lens. Blurred images with defocus distance from -5 µm to +5 µm can be virtually refocused to the in-focus plane post measurement. We demonstrate an equivalent data throughput of >2 gigapixels per second, the highest among existing WSI systems. Using the same deep neural network, we also report a high-resolution virtual staining strategy and demonstrate it for Fourier ptychographic WSI. The DeepWSI platform may provide a turnkey solution for developing high-performance diagnostic tools for digital pathology.

Pre-treatment MRI of leukaemia and lymphoma in children: are there differences in marrow replacement patterns on T1-weighted images?

OBJECTIVES: To investigate the prevalence and distribution of specific marrow patterns on pre-treatment magnetic resonance imaging (MRI) examinations in children with leukaemia and lymphoma and with respect to the anatomic location. MATERIALS AND METHODS: This retrospective IRB-approved and HIPAA-compliant study included children with leukaemia or lymphoma who underwent pre-treatment MRI examinations over 18 years (between 1 January 1995 and 31 August 2013). Two radiologists blinded to the clinical diagnosis reviewed each study to determine the presence or absence of abnormal marrow signal and, when present, sub-categorised the pattern into diffuse, patchy, or focal abnormal marrow. Chi-square and Fisher's exact tests were used to compare marrow patterns between leukaemia and lymphoma. RESULTS: The study included 50 children (32 males and 18 females; mean age 9.5 ± 5.3 years) with 54 MRI examinations (27 leukaemia and 27 lymphoma) that included 26 spine and 28 non-spine studies. Marrow replacement was present on 43 (80%) studies, significantly more common with leukaemia than with lymphoma (p = 0.039). The diffuse replacement pattern was significantly more common with leukaemia when compared to lymphoma (p < 0.001) and the focal pattern was only observed with lymphoma. In the spine, the diffuse pattern was observed with lymphoma (3/14, 21%). All patients with leukaemia and MRI outside of the spine showed marrow involvement. CONCLUSION: Marrow replacement is common on MRI from children with leukaemia and lymphoma. A diffuse pattern was significantly associated with leukaemia on studies outside of the spine and a focal pattern was only observed with lymphoma, independently of the anatomic location. KEY POINTS: • Bone marrow replacement on pre-treatment MRI examinations in children with leukaemia and lymphoma was observed in 93% (25/27) and 67% (18/27), respectively. • Diffuse pattern of marrow replacement was significantly more common in leukaemia even though this pattern was also observed with lymphoma on the spine MRI studies. • Focal pattern of marrow replacement was present only with lymphoma and not with leukaemia regardless of the anatomic location.

Applications of PET in Diagnosis and Prognosis of Leukemia.

As a malignant hematopoietic stem cell disease, leukemia remains life-threatening due to its increasing incidence rate and mortality rate. Therefore, its early diagnosis and treatment play a very important role. In the present work, we systematically reviewed the current applications and future directions of positron emission tomography (PET) in patients with leukemia, especially 18F-FDG PET/CT. As a useful imaging approach, PET significantly contributes to the diagnosis and treatment of different types of leukemia, especially in the evaluation of extramedullary infiltration, monitoring of leukemia relapse, detection of Richter's transformation (RT), and assessment of the inflammatory activity associated with acute graft versus host disease. Future investigations should be focused on the potential of PET/CT in the prediction of clinical outcomes in patients with leukemia and the utility of novel radiotracers.

Long-term in vivo imaging reveals tumor-specific dissemination and captures host tumor interaction in zebrafish xenografts.

Understanding mechanisms mediating tumor metastasis is crucial for diagnostic and therapeutic targeting. Here, we take advantage of a transparent embryonic zebrafish xenograft model (eZXM) to visualize and track metastatic cells in real time using selective plane illumination microscopy (SPIM) for up to 30 h. Injected human leukemic and breast cancer cells exhibited cell-type specific patterns of intravascular distribution with leukemic cells moving faster than breast cancer cells. Tracking of tumor cells from high-resolution images revealed acute differences in intravascular speed and distance covered by cells. While the majority of injected breast cancer cells predominantly adhered to nearby vasculature, about 30% invaded the non-vascularized tissue, reminiscent of their metastatic phenotype. Survival of the injected tumor cells appeared to be partially inhibited and time-lapse imaging showed a possible role for host macrophages of the recipient embryos. Leukemic cell dissemination could be effectively blocked by pharmacological ROCK1 inhibition using Fasudil. These observations, and the ability to image several embryos simultaneously, support the use of eZXM and SPIM imaging as a functional screening platform to identify compounds that suppress cancer cell spread and invasion.

Expert knowledge for the recognition of leukemic cells.

This work shows the advantage of expert knowledge for leukemic cell recognition. In the medical area, visual analysis of microscopic images has regularly used biological samples to recognize hematological disorders. Nowadays, techniques of image recognition are needed to achieve an adequate identification of blood tissues. This paper presents a procedure to acquire expert knowledge from blood cell images. We apply Gaussian mixtures, evolutionary computing, and standard techniques of image processing to extract knowledge. This information feeds a support vector machine or multilayer perceptron to classify healthy or leukemic cells. Additionally, convolutional neural networks are used as a benchmark to compare our proposed method with the state of the art. We use a public database of 260 healthy and leukemic cell images. Results show that our traditional pattern recognition methodology matches deep learning accuracy since the recognition of blood cells achieves 99.63%, whereas the convolutional neural networks reach 97.74% on average. Moreover, the computational effort of our approach is minimal, while meeting the requirement of being explainable.