RESUMO

Acute leukemia is usually diagnosed when a test of peripheral blood shows at least 20% of abnormal immature cells (blasts), a figure even lower in case of recurrent cytogenetic abnormalities. Blast identification is crucial for white blood cell (WBC) counting, which depends on both identifying the cell type and characterizing the cellular morphology, processes susceptible of inter- and intraobserver variability. The present work introduces an image combined-descriptor to detect blasts and determine their probable lineage. This strategy uses an intra-nucleus mosaic pattern (InMop) descriptor that captures subtle nuclei differences within WBCs, and Haralick's statistics which quantify the local structure of both nucleus and cytoplasm. The InMop captures WBC inner-nucleus structure by applying a multiscale Shearlet decomposition over a repetitive pattern (mosaic) of automatically-segmented nuclei. As a complement, Haralick's statistics characterize the local structure of the whole cell from an intensity co-occurrence matrix representation. Both InMoP and Haralick-based descriptors are calculated using the b-channel from Lab color-space. The combined-descriptor is assessed by differentiating blasts from nonleukemic cells with support vector machine (SVM) classifiers and different transformation kernels, in two public and independent databases. The first database-D1 (n = 260) is composed of healthy and acute lymphoid leukemia (ALL) single cell images, and second database-D2 contains acute myeloid leukemia (AML) blasts (n = 3294) and nonblast (n = 15,071) cell images. In a first experiment, blasts versus nonblast differentiation is performed by training with a subset of D2 (n = 6588) and testing in D1 (n = 260), obtaining a training AUC of 0.991 ± 0.002 and AUC = 0.782 for the independent validation. A second experiment automatically differentiates AML blasts (260 images from D2) from ALL blasts (260 images from D1), with an AUC of 0.93. In a third experiment, state-of-the-art strategies, VGG16 and RESNEXT convolutional neural networks (CNN), separate blast from nonblast cells in both databases. The VGG16 showed an AUC of 0.673 and the RESNEXT of 0.75. Reported metrics for all the experiments are area under the ROC curve (AUC), accuracy and F1-score.

Assuntos

Leucemia Mieloide Aguda , Humanos , Leucemia Mieloide Aguda/diagnóstico , Leucócitos , Contagem de Leucócitos , Citoplasma

RESUMO

Emerging deep learning-based applications in precision medicine include computational histopathological analysis. However, there is a lack of the required training image datasets to generate classification and detection models. This phenomenon occurs mainly due to human factors that make it difficult to obtain well-annotated data. The present study provides a curated public collection of histopathological images (DeepHP) and a convolutional neural network model for diagnosing gastritis. Images from gastric biopsy histopathological exams were used to investigate the performance of the proposed model in detecting gastric mucosa with Helicobacter pylori infection. The DeepHP database comprises 394,926 histopathological images, of which 111 K were labeled as Helicobacter pylori positive and 283 K were Helicobacter pylori negative. We investigated the classification performance of three Convolutional Neural Network architectures. The models were tested and validated with two distinct image sets of 15% (59K patches) chosen randomly. The VGG16 architecture showed the best results with an Area Under the Curve of 0.998%. The results showed that CNN could be used to classify histopathological images from gastric mucosa with marked precision. Our model evidenced high potential and application in the computational pathology field.

Assuntos

Gastrite , Infecções por Helicobacter , Helicobacter pylori , Humanos , Infecções por Helicobacter/diagnóstico , Infecções por Helicobacter/patologia , Mucosa Gástrica/patologia , Gastrite/diagnóstico , Gastrite/patologia , Gastroscopia/métodos

RESUMO

OBJECTIVE: Previous studies have reported a close relationship between malignant mesothelioma (MM) and the immune matricial microenvironment (IMM). One of the major problems in these studies is the lack of adequate adjustment for potential confounders. Therefore, the aim of this study was to identify and quantify risk factors such as IMM and various tumor characteristics and their association with the subtype of MM and survival. METHODS: We examined IMM and other tumor markers in tumor tissues from 82 patients with MM. These markers were evaluated by histochemistry, immunohistochemistry, immunofluorescence, and morphometry. Logistic regression analysis, cluster analysis, and Cox regression analysis were performed. RESULTS: Hierarchical cluster analysis revealed two clusters of MM that were independent of clinicopathologic features. The high-risk cluster included MM with high tumor cellularity, high type V collagen (Col V) fiber density, and low CD8+ T lymphocyte density in the IMM. Our results showed that the risk of death was increased for patients with MM with high tumor cellularity (OR = 1.63, 95% CI = 1.29-2.89, P = .02), overexpression of Col V (OR = 2.60, 95% CI = 0.98-6.84, P = .04), and decreased CD8 T lymphocytes (OR = 1.001, 95% CI = 0.995-1.007, P = .008). The hazard ratio for the high-risk cluster was 2.19 (95% CI = 0.54-3.03, P < .01) for mortality from MM at 40 months. CONCLUSION: Morphometric analysis of Col V, CD8+ T lymphocytes, and tumor cellularity can be used to identify patients with high risk of death from MM.

Assuntos

Biomarcadores Tumorais/análise , Mesotelioma Maligno/mortalidade , Microambiente Tumoral , Linfócitos T CD8-Positivos , Colágeno Tipo I/análise , Colágeno Tipo V/análise , Colágeno Tipo V/metabolismo , Feminino , Imunofluorescência , Humanos , Imuno-Histoquímica , Hibridização In Situ , Contagem de Linfócitos , Masculino , Mesotelioma Maligno/imunologia , Mesotelioma Maligno/metabolismo , Mesotelioma Maligno/patologia , Pessoa de Meia-Idade , Análise de Regressão , Estudos Retrospectivos , Medição de Risco , Fatores de Risco , Análise Serial de Tecidos , Microambiente Tumoral/imunologia

RESUMO

Existing computational approaches have not yet resulted in effective and efficient computer-aided tools that are used in pathologists' daily practice. Focusing on a computer-based qualification for breast cancer diagnosis, the present study proposes two deep learning architectures to efficiently and effectively detect and classify mitosis in a histopathological tissue sample. The first method consists of two parts, entailing a preprocessing of the digital histological image and a free-handcrafted-feature Convolutional Neural Network (CNN) used for binary classification. Results show that the methodology proposed can achieve 95% accuracy in testing, with an F1-score of 94.35%. This result is higher than the results using classical image processing techniques and also higher than the approaches combining CCNs with handcrafted features. The second approach is an end-to-end methodology using semantic segmentation. Results showed that this algorithm can achieve an accuracy higher than 95% in testing and an average Dice index of 0.6, higher than the existing results using CNNs (0.9 F1-score). Additionally, due to the semantic properties of the deep learning approach, an end-to-end deep learning framework is viable to perform both tasks: detection and classification of mitosis. The results show the potential of deep learning in the analysis of Whole Slide Images (WSI) and its integration to computer-aided systems. The extension of this work to whole slide images is also addressed in the last sections; as well as, some computational key points that are useful when constructing a computer-aided-system inspired by the proposed technology.