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Background and Objective: The classification of bone marrow (BM) cells by light mi- croscopy is an important cornerstone of hematological diagnosis, performed thousands of times a day by highly trained specialists in laboratories worldwide. As the manual evaluation of blood or BM smears is very time-consuming and prone to inter-observer variation, new reliable automated systems are needed. Methods: We aim to improve the automatic classification performance of hematolog- ical cell types. Therefore, we evaluate four state-of-the-art Convolutional Neural Net- work (CNN) architectures on a dataset of 171, 374 microscopic cytological single-cell images obtained from BM smears from 945 patients diagnosed with a variety of hema- tological diseases. We further evaluate the effect of an in-domain vs. out-of-domain pre-training, and assess whether class activation maps provide human-interpretable ex- planations for the models’ predictions. Results: The best performing pre-trained model (Regnet y 32gf) yields a mean pre- cision, recall, and F1 scores of 0.787 ± 0.060, 0.755 ± 0.061, and 0.762 ± 0.050, re- spectively. This is a 53.5% improvement in precision and 7.3% improvement in recall over previous results with CNNs (ResNeXt-50) that were trained from scratch. The out-of-domain pre-training apparently yields general feature extractors/filters that ap- ply very well to the BM cell classification use case. The class activation maps on cell types with characteristic morphological features were found to be consistent with the explanations of a human domain expert. For example, the Auer rods in the cytoplasm were the predictive cellular feature for correctly classified images of faggot cells. Conclusions: Our study provides data that can help hematology laboratories to choose the optimal training strategy for blood cell classification deep learning mod- els to improve computer-assisted blood and bone marrow cell identification. It also highlights the need for more specific train

ObjectivesSmall-volume biopsy-fine-needle aspiration biopsy (FNAB) with or without core biopsy-is in increasing use in diagnosis and management of lymphoma patients. Our objective was to survey the current practice in small-volume biopsy diagnosis of lymphoma, focusing on the interaction among hematopathologists and cytopathologists and the integration of FNAB, core biopsy, and flow cytometry studies at sign-out.MethodsThis study used a cross-sectional survey design employing the RedCap database distributed via nine pathology professional society email listservs. The survey consisted of 25 multiple-choice questions and several free text fields. In total, 128 pathologists participated.ResultsMost respondents indicated that FNAB specimens in which lymphoma is a diagnostic consideration (FNAB-L) are seen daily or weekly (68/116; 58.6%). However, most institutions have separate hematopathology and cytopathology services (72/116; 62.1%) with inconsistent communication. When communication occurred, respondents were frequently inclined to reconsider their original diagnoses. Barriers identified included lack of communication, inadequate access to diagnostic studies, no formal subspecialty training, and various opinions regarding FNAB in diagnosing lymphoma.ConclusionsThis survey showed that FNAB-L specimens are common, with a lack of uniformity in how complementary fine-needle aspiration and core biopsy specimens or flow immunophenotyping results are shared across hematopathology and cytopathology services.

Genomic analysis and digitalization of medical records have led to a big data scenario within hematopathology. Artificial intelligence and machine learning tools are increasingly used to integrate clinical, histopathological, and genomic data in lymphoid neoplasms. In this study, we identified global trends, cognitive, and social framework of this field from 1990 to 2020. Metadata were obtained from the Clarivate Analytics Web of Science database in January 2021. A total of 525 documents were assessed by document type, research areas, source titles, organizations, and countries. SciMAT and VOSviewer package were used to perform scientific mapping analysis. Geographical distribution showed the USA and People's Republic of China as the most productive countries, reporting up to 190 (36.19%) of all documents. A third-degree polynomic equation predicts that future global production in this area will be three-fold the current number, near 2031. Thematically, current research is focused on the integration of digital image analysis and genomic sequencing in Non-Hodgkin lymphomas, prediction of chemotherapy response and validation of new prognostic models. These findings can serve pathology departments to depict future clinical and research avenues, but also, public institutions and administrations to promote synergies and optimize funding allocation.

B-cell and T-cell lymphomas and leukemias often have distinct genetic mutations that are diagnostically defining or prognostically significant. A subset of these mutations consists of specific point mutations, which can be evaluated using genetic sequencing approaches or point mutation specific antibodies. Here, we describe genes harboring point mutations relevant to B-cell and T-cell malignancies and discuss the current availability of these targeted point mutation specific antibodies. We also evaluate the possibility of generating novel antibodies against known point mutations by computationally assessing for chemical and structural features as well as epitope antigenicity of these targets. Our results not only summarize several genetic mutations and identify existing point mutation specific antibodies relevant to hematologic malignancies, but also reveal potential underdeveloped targets which merit further study.

B-cell and T-cell lymphomas and leukemias often have distinct genetic mutations that are diagnostically defining or prognostically significant. A subset of these mutations consists of specific point mutations, which can be evaluated using genetic sequencing approaches or point mutation specific antibodies. Here, we describe genes harboring point mutations relevant to B-cell and T-cell malignancies and discuss the current availability of these targeted point mutation specific antibodies. We also evaluate the possibility of generating novel antibodies against known point mutations by computationally assessing for chemical and structural features as well as epitope antigenicity of these targets. Our results not only summarize several genetic mutations and identify existing point mutation specific antibodies relevant to hematologic malignancies, but also reveal potential underdeveloped targets which merit further study.

Lymphomatous infiltration of kidney parenchyma is a frequent complication of systemic hematologic malignancies and often shows subtle clinical presentation. Diffuse large B-cell lymphoma represents the most frequent form involving the kidney, with advanced stage at diagnosis, poor outcome, and risk for central nervous system relapse if not adequately treated. Kidney biopsy can provide specific and early detection of these cases, helping in the differential diagnosis with more frequent entities. Finally, further hematologic workup (bone marrow biopsy, complete blood cell count, and positron emission tomography) can distinguish secondary involvement of the kidney from the rarer kidney-limited forms, especially in patients without a previous diagnosis of lymphoma. Making a prompt and correct diagnosis directs the management of these cases and may improve the outcome, as described in the present report.

The following fictional case is intended as a learning tool within the Pathology Competencies for Medical Education (PCME), a set of national standards for teaching pathology. These are divided into three basic competencies: Disease Mechanisms and Processes, Organ System Pathology, and Diagnostic Medicine and Therapeutic Pathology. For additional information, and a full list of learning objectives for all three competencies, see http://journals.sagepub.com/doi/10.1177/2374289517715040.1.

Lymphomatous infiltration of kidney parenchyma is a frequent complication of systemic hematologic malignancies and often shows subtle clinical presentation. Diffuse large B-cell lymphoma represents the most frequent form involving the kidney, with advanced stage at diagnosis, poor outcome, and risk for central nervous system relapse if not adequately treated. Kidney biopsy can provide specific and early detection of these cases, helping in the differential diagnosis with more frequent entities. Finally, further hematologic workup (bone marrow biopsy, complete blood cell count, and positron emission tomography) can distinguish secondary involvement of the kidney from the rarer kidney-limited forms, especially in patients without a previous diagnosis of lymphoma. Making a prompt and correct diagnosis directs the management of these cases and may improve the outcome, as described in the present report.

Aplicat embargament des de la data de defensa fins a l'abril de 2020, The main objective of the present Doctoral Thesis is to obtain new quantitative features by means of digital image processing and machine learning for the differentiation of normal, reactive and malignant lymphoid cells of peripheral blood, contributing to an objective morphologic assessment.The research addresses the following two issues:(1)Using machine learning, geometric, color and texture descriptors are searched which have an explicit quantitative formulation and a reasonable qualitative interpretation in visual morphologic terms.(2)Considering cellular abnormalities established a priori, associated with specific diseases, the aim is to identify specific quantitative descriptors of morphological characteristics that cytologists recognize visually and usually express subjectively.More than 200 patients and 16 different lymphoid cell groups have been included in the research. Using the CellaVision DM96, 12,000 images have been acquired and using the microscope Olympus BX43, 9,000. Almost 2,700 features including geometric, color and texture (first and second-order statistics, granulometric, Wavelet and Gabor) have been analyzed for lymphoid cell differentiation. Six color spaces have been considered: RGB, CMYK, HSV, XYZ, Lab and Luv.The 20 most efficient features for the differentiation between reactive lymphoid cells (infections) and neoplastic cells (abnormal lymphocytes in lymphoma or lymphoid blasts in acute leukemia) have been analyzed. The three most relevant descriptors for the recognition of the 12 lymphoid cell groups considered are geometric: 1) nucleus/cytoplasm ratio, 2) nuclear perimeter and 3) cell diameter. Most of the 20 descriptors show significant differences between pairs of abnormal lymphocytes which are difficult to recognize by morphology. Five color and texture features are significant to discriminate reactive lymphocytes from abnormal lymphoid cells.Quantitative descriptors have been identified for the detection of specific cell morpholog, L'objectiu general de la present Tesi és obtenir nous descriptors quantitatius mitjançant processament digital d’imatges i aprenentatge automàtic per a la diferenciació de cèl·lules limfoides normals, reactives i malignes de sang perifèrica, contribuint a una anàlisi objectiva de la citologia sanguínia. La recerca s'ha enfocat des de dues perspectives: (1) Partint de l'aprenentatge automàtic, s'han buscat descriptors geomètrics, de color i de textura que tinguin una formulació quantitativa explícita i una interpretació qualitativa raonable en termes morfològics visuals. (2) Partint d'anormalitats cel·lulars establertes a priori, associades a malalties específiques, l'objectiu és identificar descriptors quantitatius específics de característiques morfològiques que els citòlegs reconeixen de forma visual i expressen habitualment amb conceptes subjectius. S'han inclòs més de 200 pacients i 16 grups cel·lulars limfoides. Amb el sistema CellaVision DM96 s'han adquirit 12.000 imatges i amb el microscopi Olympus BX43, 9.000. Per a la diferenciació cel·lular s’han analitzat 2.700 descriptors geomètrics, de color i de textura (estadístics de primer i segon ordre, granulomètrics, Wavelet i Gabor). S'han considerat sis espais de color (RGB, CMGN, HSV, XYZ, Lab i Luv). S'han analitzat els 20 descriptors geomètrics, de color i de textura més eficients per a la diferenciació entre cèl·lules limfoides reactives (infeccions) i neoplàsiques (anormals als limfomes o blasts limfoides a les leucèmies agudes). Els descriptors més rellevants per al reconeixement dels 12 grups cel·lulars limfoides són geomètrics: 1) relació nucli/citoplasma, 2) perímetre del nucli i 3) diàmetre de la cèl·lula. La majoria dels 20 descriptors mostren diferències significatives entre parelles de limfòcits anormals de difícil reconeixement per morfologia. Cinc descriptors de color i textura són significatius per discriminar els limfòcits reactius dels anormals. S'han identificat descriptors quantitatius per a, Postprint (published version)