Prostate Cancer

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 radionuclide applicator is an effective non-invasive therapeutic method. However, the inordinance of lesions is still the main challenge for precise local treatment and radiation protection of normal skins. A radionuclide applicator possessing advanced shape ad...

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 aphasia (PSA) and that this is mainly subserved by the spared parts of the left-lateralized language network. However, it remains unclear to what extent the production of rhythmic-melodic acoustic patterns in singing remains preserved in aphasia and whic...

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 ...

Fine-Tuning Open-Source Large Language Models to Improve Their Performance on Radiation Oncology Tasks: A Feasibility Study to Investigate Their Potential Clinical Applications in Radiation Oncology

Background: The radiation oncology clinical practice involves many steps relying on the dynamic interplay of abundant text data. Large language mode...

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging

Prostate cancer (PCa) is the most prevalent cancer among men in the United States, accounting for nearly 300,000 cases, 29% of all diagnoses and 35,...

A memory-driven reinforcement learning model of phenotypic adaptation for anticipating therapeutic resistance in prostate cancer

While contemporary cancer treatment strategies have significantly prolonged the lives of patients, therapeutic resistance remains a predominant cause ...

Radiomics-Based Early Triage of Prostate Cancer: A Multicenter Study from the CHAIMELEON Project

Prostate cancer (PCa) is the most commonly diagnosed malignancy in men worldwide. Accurate triage of patients based on tumor aggressiveness and stagin...

A multi-modal deep learning framework for predicting PSA progression-free survival in metastatic prostate cancer using PSMA PET/CT imaging

PSMA PET/CT imaging has been increasingly utilized in the management of patients with metastatic prostate cancer (mPCa). Imaging biomarkers derived fr...

Artificial Intelligence quantified prostate specific membrane antigen imaging in metastatic castrate-resistant prostate cancer patients treated with Lutetium-177-PSMA-617

The VISION study1 found that Lutetium-177 (177Lu)–PSMA-617 (“Lu-177”) improved overall survival in metastatic castrate resistant prostate cancer (mCRP...