Clinico-Haematological Profile of Acute Megakaryoblastic Leukaemia: Report of Five Cases

Acute megakaryoblastic leukemia (AMKL) is a rare subtype of acute myeloid leukemia. Although known as a distinct entity for a very long time, because of lack of distinct clinical features and morphological criteria, it is difficult to diagnose this variant correctly. We herein present the clinical, morphological, cytochemical, and immunocytochemical features of five cases of AMKL. Certain morphological features such as presence of abnormal platelet count, giant platelets, and cytoplasmic blebbing in blasts were found to be important pointers towards the diagnosis. However, none of the features were found to be consistent and thus morphological diagnosis has to be confirmed by cytochemistry and immunocytochemistry

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) 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 in TILs 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 TNBC

Cytomorphology and immunohistochemistry of extrarenal rhabdoid tumor: A case report with review of literature

Extrarenal rhabdoid tumor (ERRT) is a rare, aggressive tumor with extremely poor prognosis. We report a case of ERRT with intraspinal extension in a 1.5-year-old child diagnosed by fine needle aspiration cytology (FNAC) and immunohistochemistry. The child presented with a right lumbar region lump of two months duration. Ultrasound guided FNAC was performed and cell block was prepared. Smears were highly cellular and showed a dispersed population of large round cells having abundant pale eosinophillic cytoplasm, centrally to eccentrically placed nucleus with large prominent nucleoli. Immunohistochemistry was carried out on cell block which was positive for epithelial membrane antigen EMA and Vimentin. It was negative for leucocyte common antigen [LCA], wilms tumor 1, WT1, desmin and neuron specific enolaseNSE, thus ruling out other tumors like lymphoma, Wilms tumor, rhabdomyosarcoma, and neuroblastoma. A final diagnosis of ERRT was given. ERRT is an extremely rare tumor of retroperitoneal area; it should be included in the differential diagnosis of malignant round cell tumor in children. Cell block in this case is mandatory for putting up the panel of immunohistochemistry which can clinch the diagnosis of rhabdoid tumor and treatment can be started as early as possible

Isolated spinal neurosarcoidosis: An enigmatic intramedullary spinal cord pathology-case report and review of the literature

Isolated spinal cord neurosarcoidosis (NS) in the absence of systemic disease or intracranial involvement is exceptionally rare. Adjunctive laboratory tests though useful may not be reliable and the absence of any pathognomonic radiological features makes the diagnosis difficult. As spinal cord NS may be a presenting feature of systemic sarcoidosis which may be occult on routine workup, 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) may be of value in unraveling this systemic involvement avoiding biopsying the spinal cord. A case of truly isolated NS is described with review of literature on this enigmatic pathology. Long segment intramedullary signal changes with focal parenchymal along with dural/meningeal enhancement in the absence of significant cervical stenosis in a young patient of northern European or African-American decent is very suggestive of NS and although may be presumably treated with steroids; there should be a low threshold for spinal cord biopsy especially in the absence of response to steroids to confirm isolated spinal cord NS in a patient with clinical neurological deterioration

Automated analysis of computerized morphological features of cell clusters associated with malignancy on bile duct brushing whole slide images

Abstract Background Bile duct brush specimens are difficult to interpret as they often present inflammatory and reactive backgrounds due to the local effects of stricture, atypical reactive changes, or previously installed stents, and often have low to intermediate cellularity. As a result, diagnosis of biliary adenocarcinomas is challenging and often results in large interobserver variability and low sensitivity Objective In this work, we used computational image analysis to evaluate the role of nuclear morphological and texture features of epithelial cell clusters to predict the presence of pancreatic and biliary tract adenocarcinoma on digitized brush cytology specimens. Methods Whole slide images from 124 patients, either diagnosed as benign or malignant based on clinicopathological correlation, were collected and randomly split into training (ST, N = 58) and testing (Sv, N = 66) sets, with the exception of cases diagnosed as atypical on cytology were included in Sv. Nuclear boundaries on cell clusters extracted from each image were segmented via a watershed algorithm. A total of 536 quantitative morphometric features pertaining to nuclear shape, size, and aggregate cluster texture were extracted from within the cell clusters. The most predictive features from patients in ST were selected via rank‐sum, t‐test, and minimum redundancy maximum relevance (mRMR) schemes. The selected features were then used to train three machine‐learning classifiers. Results Malignant clusters tended to exhibit lower textural homogeneity within the nucleus, greater textural entropy around the nuclear membrane, and longer minor axis lengths. The sensitivity of cytology alone was 74% (without atypicals) and 46% (with atypicals). With machine diagnosis, the sensitivity improved to 68% from 46% when atypicals were included and treated as nonmalignant false negatives. The specificity of our model was 100% within the atypical category. Conclusion We achieved an area under the receiver operating characteristic curve (AUC) of 0.79 on Sv, which included atypical cytological diagnosis