Publications by authors named "Z Khodabakhshi"
Article Synopsis
- The study investigates how differences in data from multiple hospitals affect the effectiveness of deep learning models for automatically segmenting brain metastases (BM) and evaluates a technique called "learning without forgetting" (LWF) to enhance model adaptability without needing to share sensitive data.
- Six datasets from various universities were analyzed, and results showed that training on data from just one center provided diverse performance levels, while training on mixed data from multiple centers generally improved outcomes, especially for certain institutions.
- The findings suggest that LWF outperformed traditional transfer learning methods in maintaining high sensitivity and precision during training, indicating it could be a valuable strategy for training models while addressing privacy concerns, despite challenges from data variability.
Background And Purpose: Magnetic resonance imaging (MRI) scans are highly sensitive to acquisition and reconstruction parameters which affect feature stability and model generalizability in radiomic research. This work aims to investigate the effect of image pre-processing and harmonization methods on the stability of brain MRI radiomic features and the prediction performance of radiomic models in patients with brain metastases (BMs).
Materials And Methods: Two T1 contrast enhanced brain MRI data-sets were used in this study.
Defining the exact histological features of salivary gland malignancies before treatment remains an unsolved problem that compromises the ability to tailor further therapeutic steps individually. Radiomics, a new methodology to extract quantitative information from medical images, could contribute to characterizing the individual cancer phenotype already before treatment in a fast and non-invasive way. Consequently, the standardization and implementation of radiomic analysis in the clinical routine work to predict histology of salivary gland cancer (SGC) could also provide improvements in clinical decision-making.
View Article and Find Full Text PDFAims: We aimed to build radiomic models for classifying non-small cell lung cancer (NSCLC) histopathological subtypes through a dual-centre dataset and comprehensively evaluate the effect of ComBat harmonisation on the performance of single- and multimodality radiomic models.
Materials And Methods: A public dataset of NSCLC patients from two independent centres was used. Two image fusion methods, namely guided filtering-based fusion and image fusion based on visual saliency map and weighted least square optimisation, were used.
The papain-like protease (PLpro) plays a critical role in SARS-CoV-2 (SCoV-2) pathogenesis and is essential for viral replication and for allowing the virus to evade the host immune response. Inhibitors of PLpro have great therapeutic potential, however, developing them has been challenging due to PLpro's restricted substrate binding pocket. In this report, we screened a 115 000-compound library for PLpro inhibitors and identified a new pharmacophore, based on a mercapto-pyrimidine fragment that is a reversible covalent inhibitor (RCI) of PLpro and inhibits viral replication in cells.
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