Introduction:Fine-needle aspiration cytology (FNAC) for identification of papillary carcinoma thyroid is a moderately sensitive and specific modality. The present machine learning tools can correctly classify images into broad categories. Training software for recognition of papillary thyroid carcinoma on FNAC smears will be a decisive step toward automation of cytopathology.Aim:The aim of this study is to develop an artificial neural network (ANN) for the purpose of distinguishing papillary carcinoma thyroid and nonpapillary carcinoma thyroid on microphotographs from thyroid FNAC smears.Subjects and Methods:An ANN was developed in the Python programming language. In the training phase, 186 microphotographs from Romanowsky/Pap-stained smears of papillary carcinoma and 184 microphotographs from smears of other thyroid lesions (at ×10 and ×40 magnification) were used for training the ANN. After completion of training, performance was evaluated with a set of 174 microphotographs (66 – nonpapillary carcinoma and 21 – papillary carcinoma, each photographed at two magnifications ×10 and ×40).Results:The performance characteristics and limitations of the neural network were assessed, assuming FNAC diagnosis as gold standard. Combined results from two magnifications showed good sensitivity (90.48%), moderate specificity (83.33%), and a very high negative predictive value (96.49%) and 85.06% diagnostic accuracy. However, vague papillary formations by benign follicular cells identified wrongly as papillary carcinoma remain a drawback.Conclusion:With further training with a diverse dataset and in conjunction with automated microscopy, the ANN has the potential to develop into an accurate image classifier for thyroid FNACs.

Intracranial teratomas represent a rare lesion accounting for 0.1%–0.7% of all intracranial tumors. Those in the fourth ventricle have rarely been reported. The present case is that of a 28-year-old man with occipital headache for two months. MRI examination revealed a well-defined extra-axial cystic lesion in posterior fossa in the midline herniating through the foramen magnum. Pre operatively, the mass was seen to be occupying the whole of the posterior fossa and arising from the roof of the fourth ventricle. On gross examination, the lesion had both solid and cystic components. Histopathological examination showed multiple cystic areas lined by brain tissue admixed with islands of cartilage and salivary gland elements and intestinal type glands. A diagnosis of mature cystic teratoma was made.

Background: The conventional Papanicolaou-stained cervical smear is the most common screening test for cervical cancer. The sensitivity of the test in detecting abnormal cells is 67e75% in various studies. Owing to the volume of smears at cancer screening centres, significant man-hours are expended in the test. We have developed a software program for identification of foci of abnormal cells from conventional smears. We have chosen the convolutional neural network (CNN) model for its efficacy in image classification.Methods: A total of 1838 microphotographs from cervical smears, containing 1301 'normal' foci and 537 'abnormal' foci were included in the study. The data set was split into training, testing and validation sets. A CNN was developed in the Python programming language.The CNN was trained with the training and testing set. At the end of training, 94.64% accuracy was achieved in the testing set. The CNN was then run on the validation set (441 images). Results:The CNN showed 94.28% sensitivity, 96.01% specificity, 91.66% positive predictive value and 97.30% negative predictive value. The CNN could recognise normal squamous cells, overlapping cells, neutrophils and debris and classify the focus appropriately. False positives were reported when the CNN failed to recognise overlapping cells (2.7% microphotographs). It could correctly label cell clusters with high nuclear cytoplasmic ratio and hyperchromasia. In 1.8% of microphotographs, a false negative was reported. Conclusion:The CNN showed 95.46% diagnostic accuracy, suggesting potential use in screening.

Introduction: Body fluid cytology is one of the commonest investigations performed in indoor patients, both for diagnosis of suspected carcinoma as well as staging of known carcinoma. Carcinoma is diagnosed in body fluids by the pathologist through microscopic examination and searching for malignant epithelial cell clusters. The process of screening body fluid smears is a time consuming and error prone process. Aim: We have attempted to construct a machine learning model which can screen body fluid cytology smears for malignant cells. Materials and methods: MGG stained Ascitic / pleural fluid cytology smears were included from 21 cases (14 malignant, 07 benign) in this study. A total of 693 microphotographs were taken at 40x magnification at the same illumination and after correction of white balance. A Magnus Microphotography system was used for photography. The images were split into the training set (195 images), test set (120 images) and validation set (378 images). A machine learning model, a convolutional neural network, was developed in the Python programming language using the Keras deep learning library. The model was trained with the images of the training set. After completion of training, the model was evaluated on the test set of images. Results: Evaluation of the model on the test set produced a sensitivity of 97.87%, specificity 85.26%, PPV 95.18%, NPV 93.10% In 06 images, the model has failed to detect singly scattered malignant cells/ clusters. 14 (3.7%) false positives was reported by the model. The machine learning model shows potential utility as a screening tool. However, it needs improvement in detecting singly scattered malignant cells and filtering inflammatory infiltrate.

Background: Lymph node fine needle aspiration cytology (FNAC) is the first line investigation for evaluation of lymph node disease. Existing literature reports high degree of correlation between lymph node FNAC and histological examination. The aim of the present study is to re-evaluate the diagnostic accuracy of FNAC in view of frequent discordance between FNAC and diagnosis on biopsy.Methods: Among a total of 495 lymph node FNACs and 291 biopsies, 69 adequate FNACs which were followed up with biopsy were evaluated with standard statistical methods for assessment of diagnostic accuracy.Results: The commonest diagnosis on biopsy was reactive lymph node (34.71%) followed by granulomatous disease (26.12%) and lymphoid neoplasms (20.96%). Reactive lymphadenitis and granulomatous disease were also the two commonest categories on FNAC (34.34% and 24.85% respectively). However, the sensitivity of FNAC in diagnosis of granulomatous disease was found to be 45.83%, which increases to 70.03% if necrosis is included as a marker of granulomatous disease. The greatest sensitivity was achieved in diagnosis of metastatic disease (88.89%), followed by lymphoid neoplasms (69.23%).Conclusions: FNAC is a useful tool for excluding specific categories of lymph node diseases, esp. metastatic disease. However, the technique needs improvement as to sample more representative areas of the node, to improve its sensitivity.