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Faisal Yaseen
University of Washington
Uncertainty-Aware Multimodal Biomarker-Guided Treatment Response Prediction: Integrating FDG PET, T-cell Repertoire, and Inflammatory Cytokines with Conformal Uncertainty Quantification
Uncertainty-aware multimodal biomarker-guided treatment response prediction in metastatic NSCLC remains a critical unmet need to support robust therapy selection and adaptation over time. We developed a multimodal framework integrating FDG-PET, T-cell recepto...
Poster Program · Therapy Physics
A Cgan-Based Spatially-Resolved Prediction of Lung Tumor Response to Chemoradiation
Lung cancer is a leading global malignancy with high mortality. Radiotherapy is a critical treatment; however, current planning often suffers from subjective dose settings and side effects. This study aims to use a Conditional Generative Adversarial Network (...
Poster Program · Radiopharmaceuticals, Theranostics, and Nuclear Medicine
Uncertainty-Aware Multiscale Patient- and Voxel-Level Treatment Response Forecasting for Personalized Radiotherapy Using Conformal Prediction In Advanced Non-Small Cell Lung Cancer (NSCLC)
Multiscale treatment response prediction in advanced NSCLC enables spatially informed dose painting, yet prediction point estimates alone do not convey the uncertainty required for adaptive therapy decision support. We developed a multiscale conformal predict...
Poster Program · Therapy Physics
BLUE RIBBON POSTER RADIOPHARMACEUTICALS: Attention Med3D: A 3D Deep Learning Model Based on Transfer Learning and Attention Mechanism for Predicting Mid-Treatment Chemoradiation Response on FDG PET of La-NSCLC
Accurate assessment of early radiotherapy response in tumors provides crucial guidance for optimizing radiotherapy protocols. We developed a 3D deep learning model termed Attention Med3D based on transfer learning and attention mechanisms for predicting mid-t...
Poster Program · Radiopharmaceuticals, Theranostics, and Nuclear Medicine
Clustering Model Based on 3DUNET-Gmm Combining 3D-Unet Feature Extracting with Gmm Clustering to Divide Tumor Subregions on FDG PET for La-NSCLC
Accurate identification of high-risk and low-risk tumor subregions enables radiographers to customize radiation dose distributions for biologically adaptive therapies. This study proposes a 3DUNET-GMM model that integrates 3D-UNet feature extracting with Gaus...
Proffered Program · Radiopharmaceuticals, Theranostics, and Nuclear Medicine
Dual Path Networks for Voxel-Level Prediction of Mid-Chemoradiotherapy Response In FDG PET for Locally Advanced Non-Small Cell Lung Cancer
Accurate prediction of tumor response during chemoradiotherapy is essential for treatment optimization but remains challenging. We developed a deep learning model based on a Dual Path Network (DPN), which is a hybrid architecture combining elements of ResNet...
Proffered Program · Radiopharmaceuticals, Theranostics, and Nuclear Medicine