Tata Memorial Hospital

About: Tata Memorial Hospital is a healthcare organization based out in Mumbai, India. It is known for research contribution in the topics: Cancer & Breast cancer. The organization has 3187 authors who have published 4636 publications receiving 109143 citations.

Growing teratoma syndrome

Abstract: Growing teratoma syndrome is the term applied to enlarging retroperitoneal or other metastatic masses containing mature teratoma during chemotherapy for nonseminomatous germ cell tumors. Four cases of the growing teratoma syndrome are presented, the metastatic masses being in the retroperitoneal in all the cases. All these patients had enlarging retroperitoneal masses in the presence of normal serum biomarkers following chemotherapy for nonseminomatous tumors. Surgical excision was carried out in all four patients, with disease free survivals ranging from 6 to 24 months after surgery.

Overall Survival Prediction in Glioblastoma With Radiomic Features Using Machine Learning.

Abstract: Glioblastoma is a WHO grade IV brain tumor, which leads to poor overall survival (OS) of patients. For precise surgical and treatment planning, OS prediction of glioblastoma (GBM) patients is highly desired by clinicians and oncologists. Radiomic research attempts at predicting disease prognosis, thus providing beneficial information for personalized treatment from a variety of imaging features extracted from multiple MR images. In this study, first-order, intensity-based volume and shape-based and textural radiomic features are extracted from fluid-attenuated inversion recovery (FLAIR) and T1ce MRI data. The region of interest is further decomposed with stationary wavelet transform with low-pass and high-pass filtering. Further, radiomic features are extracted on these decomposed images, which helped in acquiring the directional information. The efficiency of the proposed algorithm is evaluated on Brain Tumor Segmentation (BraTS) challenge training, validation, and test datasets. The proposed approach achieved 0.695, 0.571, and 0.558 on BraTS training, validation, and test datasets. The proposed approach secured the third position in BraTS 2018 challenge for the OS prediction task.