Prostate Cancer

Automated Treatment Planning for Interstitial HDR Brachytherapy for Locally Advanced Cervical Cancer using Deep Reinforcement Learning

High-dose-rate (HDR) brachytherapy plays a critical role in the treatment of locally advanced cervical cancer but remains highly dependent on manual treatment planning expertise. The objective of this study is to develop a fully automated HDR brachytherapy planning framework that integrates reinforcement learning (RL) and dose-based optimization to generate clinically acceptable treatment plans ...

Automatic Treatment Planning using Reinforcement Learning for High-dose-rate Prostate Brachytherapy

Purpose: In high-dose-rate (HDR) prostate brachytherapy procedures, the pattern of needle placement solely relies on physician experience. We investigated the feasibility of using reinforcement learning (RL) to provide needle positions and dwell times based on patient anatomy during pre-planning stage. This approach would reduce procedure time and ensure consistent plan quality. Materials and Me...

A Large Convolutional Neural Network for Clinical Target and Multi-organ Segmentation in Gynecologic Brachytherapy with Multi-stage Learning

Purpose: Accurate segmentation of clinical target volumes (CTV) and organs-at-risk is crucial for optimizing gynecologic brachytherapy (GYN-BT) trea...

Prostate Cancer Screening with Artificial Intelligence-Enhanced Micro-Ultrasound: A Comparative Study with Traditional Methods

Background and objective: Micro-ultrasound (micro-US) is a novel imaging modality with diagnostic accuracy comparable to MRI for detecting clinicall...

Photon-counting CT in cancer radiotherapy: technological advances and clinical benefits.

Photon-counting computed tomography (PCCT) marks a significant advancement over conventional Energy-integrating detector CT systems. This review highl...

A sub-meter resolution urban surface albedo dataset for 34 U.S. cities based on deep learning.

Surface albedo is a key determinant of urban heat islands, which modulates the amount of solar energy absorbed or reflected by urban surfaces, influen...

A customizable P hydrogel applicator for brachytherapy of skin hemangioma based on machine learning and 3D-printing.

Skin hemangioma is a tumor originating from skin blood vessels, which often occurs in infants and children. Brachytherapy with the P-based radionuclid...

Neural pathways underlying the production of pitch and rhythm in aphasia.

Singing is a universal human attribute. Previous studies suggest that the ability to produce words through singing can be preserved in poststroke apha...

Bioconjugates of photon-upconversion nanoparticles with antibodies for the detection of prostate-specific antigen and p53 in heterogeneous and homogeneous immunoassays.

Sensitive immunoassays for the detection of tumor biomarkers play an important role in the early diagnosis and therapy of cancer. Using luminescent na...

Enhancing bone metastasis prediction in prostate cancer using quantitative mpMRI features, ISUP grade and PSA density: a machine learning approach.

PURPOSE: Bone metastasis is a critical complication in prostate cancer, significantly impacting patient prognosis and quality of life. This study aims...

Evaluating prostate cancer diagnostic methods: The role and relevance of digital rectal examination in modern era.

This review examines diagnostic methods for prostate cancer, focusing on the role of digital rectal examination (DRE) alongside modern advancements li...

Validation of a Digital Pathology-Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy.

PURPOSE: A multimodal artificial intelligence (MMAI) biomarker was developed using clinical trial data from North American men with localized prostate...

Predicting Prostate Cancer Diagnosis Using Machine Learning Analysis of Healthcare Utilization Patterns.

This study investigated healthcare utilization patterns prior to prostate cancer diagnoses, aiming to develop machine learning models for early predic...

Using Machine Learning to Predict Survival in Patients with Metastatic Castration-Resistant Prostate Cancer.

Non-specific clinical biomarkers have been shown to help identify prognostic risks in cancer patients. However, the accuracy of prognostic biomarkers ...

PSA-MIL: A Probabilistic Spatial Attention-Based Multiple Instance Learning for Whole Slide Image Classification

Whole Slide Images (WSIs) are high-resolution digital scans widely used in medical diagnostics. WSI classification is typically approached using Mul...

Weakly Supervised Spatial Implicit Neural Representation Learning for 3D MRI-Ultrasound Deformable Image Registration in HDR Prostate Brachytherapy

Purpose: Accurate 3D MRI-ultrasound (US) deformable registration is critical for real-time guidance in high-dose-rate (HDR) prostate brachytherapy. ...

LAPIG: Language Guided Projector Image Generation with Surface Adaptation and Stylization

We propose LAPIG, a language guided projector image generation method with surface adaptation and stylization. LAPIG consists of a projector-camera ...

Leveraging Machine Learning and Deep Learning Techniques for Improved Pathological Staging of Prostate Cancer

Prostate cancer (Pca) continues to be a leading cause of cancer-related mortality in men, and the limitations in precision of traditional diagnostic...

Prostate-Specific Foundation Models for Enhanced Detection of Clinically Significant Cancer

Accurate prostate cancer diagnosis remains challenging. Even when using MRI, radiologists exhibit low specificity and significant inter-observer var...

Prediction of Prostate Cancer Grades Using Radiomic Features.

We developed a machine learning model for predicting prostate cancer (PCa) grades using radiomic features of magnetic resonance imaging. 112 patients ...