Publications by authors named "Deepa Dalal"
Objectives: We evaluate MR radiomics and develop machine learning-based classifiers to predict MYCN amplification in neuroblastomas.
Methods: A total of 120 patients with neuroblastomas and baseline MR imaging examination available were identified of whom 74 (mean age ± standard deviation [SD] of 6 years and 2 months ± 4 years and 9 months; 43 females and 31 males, 14 MYCN amplified) underwent imaging at our institution. This was therefore used to develop radiomics models.
Objectives: To determine the role of apparent diffusion coefficient (ADC) histogram analysis in the identification of MYCN-amplification status in neuroblastomas.
Methods: We retrospectively evaluated imaging records from 62 patients with neuroblastomas (median age: 15 months (interquartile range (IQR): 7-24 months); 38 females) who underwent magnetic resonance imaging at our institution before the initiation of any therapy or biopsy. Fourteen patients had MYCN-amplified (MYCNA) neuroblastoma.
Article Synopsis
- The study focuses on creating an automated system for measuring tumor sizes in pediatric brain tumors using MRI imagery, which is important for assessing treatment responses.
- A deep learning model, specifically a 3D U-Net, was trained on a large dataset to perform tumor segmentation and size measurement, and its results were compared with those of expert human raters.
- The findings show strong agreement between the automated system and manual assessments, suggesting that the tool could enhance accuracy and efficiency in monitoring tumor response in pediatric patients, though further validation is needed.
Background: Radiologists have difficulty distinguishing benign from malignant bone lesions because these lesions may have similar imaging appearances. The purpose of this study was to develop a deep learning algorithm that can differentiate benign and malignant bone lesions using routine magnetic resonance imaging (MRI) and patient demographics.
Methods: 1,060 histologically confirmed bone lesions with T1- and T2-weighted pre-operative MRI were retrospectively identified and included, with lesions from 4 institutions used for model development and internal validation, and data from a fifth institution used for external validation.