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Updated: May 29, 2026

Tumorsphere Derivation and Treatment from Primary Tumor Cells Isolated from Mouse Rhabdomyosarcomas
Published on: September 13, 2019
Malignant small round cell tumors
Arvind Rajwanshi1, Radhika Srinivas, Gautam Upasana
1Department of Cytology and Gynecologic Pathology, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
Abstract:
Malignant small round cell tumors are characterised by small, round, relatively undifferentiated cells. They generally include Ewing's sarcoma, peripheral neuroectodermal tumor, rhabdomyosarcoma, synovial sarcoma, non-Hodgkin's lymphoma, retinoblastoma, neuroblastoma, hepatoblastoma, and nephroblastoma or Wilms' tumor. Other differential diagnoses of small round cell tumors include small cell osteogenic sarcoma, undifferentiated hepatoblastoma, granulocytic sarcoma, and intraabdominal desmoplastic small round cell tumor. Differential diagnosis of small round cell tumors is particularly difficult due to their undifferentiated or primitive character. Tumors that show good differentiation are generally easy to diagnose, but when a tumor is poorly differentiated, identification of the diagnostic, morphological features is difficult and therefore, no definitive diagnosis may be possible. As seen in several study reports, fine needle aspiration cytology (FNAC) has become an important modality of diagnosis for these tumors. The technique yields adequate numbers of dissociated, viable cells, making it ideally suitable for ancillary techniques. Typically, a multimodal approach is employed and the principal ancillary techniques that have been found to be useful in classification are immunohistochemistry and immunophenotyping by flow cytometry, reverse transcriptase polymerase chain reaction (RT-PCR), fluorescence in situ hybridization (FISH), and electron microscopy. However, the recent characterization of chromosomal breakpoints and the corresponding genes involved in malignant small round cell tumors means that it is possible to use molecular genetic approaches for detection.
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