A5092134392- Radiomics and Machine Learning in Medical Imaging
- Advanced Neural Network Applications
- Advanced X-ray and CT Imaging
- Brain Tumor Detection and Classification
- Glioma Diagnosis and Treatment
- Medical Imaging and Analysis
- Renal cell carcinoma treatment
- Artificial Intelligence in Healthcare and Education
- Head and Neck Cancer Studies
- Lung Cancer Diagnosis and Treatment
- AI in cancer detection
- Colorectal Cancer Screening and Detection
Brigham and Women's Hospital
2024-2025
Harvard University
2024-2025
Oslo University Hospital
2023-2025
Dana-Farber Brigham Cancer Center
2024-2025
Dana-Farber Cancer Institute
2024-2025
University of Oslo
2023-2024
Boston Children's Hospital
2024
Mass General Brigham
2024
Abstract Background Postoperative recurrence risk for pediatric low-grade gliomas (pLGGs) is challenging to predict by conventional clinical, radiographic, and genomic factors. We investigated if deep learning (DL) of magnetic resonance imaging (MRI) tumor features could improve postoperative pLGG stratification. Methods used a pretrained DL tool designed segmentation extract from preoperative T2-weighted MRI patients who underwent surgery (DL-MRI features). Patients were pooled 2...
Glioblastoma Multiforme (GBM) is the most common and aggressive primary adult brain tumor, with a median overall survival of 15 months. The peritumoral zone (PBZ), region surrounding GBM, may be associated tumor infiltration aggressiveness has been recognized as clinically prognostically important. This study investigates quantitative imaging analysis PBZ its impact on in GBM using deep learning (DL). We conducted retrospective BraTS 2021 (1,251 subjects) 2020 (235 datasets, both containing...
Background and Objective: Renal cell carcinoma represents a significant global health challenge with low survival rate. The aim of this research was to devise comprehensive deep-learning model capable predicting probabilities in patients renal by integrating CT imaging clinical data addressing the limitations observed prior studies. is facilitate identification requiring urgent treatment. Methods: proposed framework comprises three modules: 3D image feature extractor, variable selection,...
Abstract BACKGROUND Disease progression is challenging to predict following surgery for pediatric low-grade glioma (pLGG), and early indicators on MRI surveillance imaging would help guide management. Longitudinal data may capture subtle temporal tumor changes patterns that could inform recurrence risk but are difficult synthesize clinically. We applied deep, self-supervised learning longitudinal short-interval event-free-survival (EFS) from time-of-scan. METHODS retrospectively collected...
This paper presents a deep learning (DL) approach for predicting survival probabilities of renal cancer patients based solely on preoperative CT imaging. The proposed consists two networks: classifier- and survival- network. classifier attempts to extract features from 3D scans predict the ISUP grade Renal cell carcinoma (RCC) tumors, as defined by International Society Urological Pathology (ISUP). Our is convolutional neural network avoid losing crucial information interconnection slides in...
2066 Background: Pediatric low-grade gliomas (pLGGs) have heterogeneous clinical presentations and prognoses. Given the morbidity of treatment, some suspected pLGGs, especially those found incidentally, are surveilled without though natural histories these tumors yet to be systematically studied. We leveraged deep learning multi-institutional data methodically analyze longitudinal volumetric trajectories pLGGs on surveillance, yielding insights into their growth implications. Methods:...
Abstract BACKGROUND Pediatric low-grade gliomas (pLGGs) have heterogeneous clinical presentations and prognoses. Given the morbidity of treatment, suspected pLGGs are surveilled without though natural histories these tumors yet to be systematically studied. METHODS We conducted a pooled, retrospective study pLGG patients diagnosed between 1992 2020 from two sources (Dana-Farber Cancer Institute/Boston Children’s Hospital Brain Tumor Network), who were untreated for at least one-year...
Renal cell carcinoma represents a significant global health challenge with low survival rate. This research aimed to devise comprehensive deep-learning model capable of predicting probabilities in patients renal by integrating CT imaging and clinical data addressing the limitations observed prior studies. The aim is facilitate identification requiring urgent treatment. proposed framework comprises three modules: 3D image feature extractor, variable selection, prediction. extractor module,...