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Osteoblastoma (OB) is an uncommon benign bone-forming tumor accounting for <1% of all bone neoplasms. Unlike conventional OB, its small subset variant “Epithelioid osteoblastoma (EO)” is characterized by its propensity for local invasion and recurrent behavior. This rare variant of an uncommon tumor when occurs in an atypical site can lead to diagnostic problems more so due to ambiguous clinico-radiologic presentation. This was what faced in the present case of 18-year-old female with a swelling in upper jaw. OB is usually more common in males and involves primarily the posterior element of the spine and the sacrum (40–55%). Less frequently, long bones of limbs are involved. Clinical, radiological and histopathological correlation in this case guided us to reach at right diagnosis of EO which helped the patient in getting correct treatment which involves surgical excision over conventional curettage. The purpose behind this case presentation is to improve the awareness about this recurrent tumor variant which has many close differentials including well-differentiated osteoblastic osteosarcoma.

Primary primitive neuroectodermal tumors (PNETs) of the kidney are quite rare and can be mistaken for a wide variety of other small round blue cell tumors which includes rhabdomyosarcoma, Wilm's tumor, carcinoid, neuroblastoma, clear cell sarcoma of the kidney, lymphoma etc. Renal Ewings/PNET can occur in the age group from 4 to 61 years. Approximately, 90% of Ewing sarcoma (ES)/PNET have a specific t(11;22) which results in a chimeric EWS-FLI-1 fusion protein. Immunohistochemical for the carboxy-terminus of FLI-1 is sensitive and highly specific for the diagnosis of ES/PNET. Herein, we have an interesting presentation in a 23-year-old male who came with neck pain and progressive quadriparesis and was diagnosed as a case of poorly differentiated malignant tumor with a differential of lymphoma versus metastatic renal cell carcinoma. The patient's condition deteriorated fast and he had a rapid downhill course. The final diagnosis of Ewings/PNET was confirmed at autopsy.

A 23-year-old pregnant lady presented with dark raised lesions over face, axillae, and upper limbs of 15-day duration. She was 35 weeks pregnant at the time of onset of the lesions. Dermatological examination revealed hyperpigmented plaques on the face and papules with raised borders in the axillae and proximal arms. Skin biopsy from both the lesions revealed a diagnosis of porokeratosis. She was treated with emollients alone and the lesions regressed four weeks following delivery. This case is being reported for the rare occurrence of the combination of disseminated superficial porokeratosis with giant porokeratosis in pregnancy.

EGFR mutations and ALK gene rearrangement was found to be mutually exclusive. Incidence of EGFR mutations (35.5%) is much higher in Indian population than in Caucasians (13%) and is close to the incidence in East Asian countries. The 7.6% incidence of ALK fusion oncogene in Indian patients establishes the importance of molecular studies to give maximum benefit of targeted therapy to the patients.

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.

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