AIMC Topic: Prostatic Neoplasms

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A systematic review of AI as a digital twin for prostate cancer care.

Computer methods and programs in biomedicine
Artificial Intelligence (AI) and Digital Twin (DT) technologies are rapidly transforming healthcare, offering the potential for personalized, accurate, and efficient medical care. This systematic review focuses on the intersection of AI-based digital...

Real-Time Dose-Guided Radiation Therapy.

International journal of radiation oncology, biology, physics
Dramatic strides have been made in real-time adaptive radiation therapy, where treating single tumors as dynamic but rigid bodies has demonstrated a halving of toxicities for prostate cancer. However, the human body is much more complex than a rigid ...

Combining Spatial Transcriptomics, Pseudotime, and Machine Learning Enables Discovery of Biomarkers for Prostate Cancer.

Cancer research
UNLABELLED: Early cancer diagnosis is crucial but challenging owing to the lack of reliable biomarkers that can be measured using routine clinical methods. The identification of biomarkers for early detection is complicated by each tumor involving ch...

Multiclass ensemble framework for enhanced prostate gland Segmentation: Integrating Self-ONN decoders with EfficientNet.

Computers in biology and medicine
Digital pathology relies on the morphological architecture of prostate glands to recognize cancerous tissue. Prostate cancer (PCa) originates in walnut shaped prostate gland in the male reproductive system. Deep learning (DL) pipelines can assist in ...

Prostate cancer prediction through a hybrid deep learning method applied to histopathological image.

Expert review of anticancer therapy
BACKGROUND: Prostate Cancer (PCa) is a severe disease that affects males globally. The Gleason grading system is a widely recognized method for diagnosing the aggressiveness of PCa using histopathological images. This system evaluates prostate tissue...

Clinical advantages in providing artificial intelligence-assisted prostate cancer diagnosis: A pilot study.

Pathology, research and practice
Prostate cancer is a prevalent male malignancy, with increasing incidence rates placing significant diagnostic burdens on pathology services worldwide. Artificial intelligence (AI) is emerging as a promising aid in enhancing diagnostic efficiency and...

Evaluating an information theoretic approach for selecting multimodal data fusion methods.

Journal of biomedical informatics
OBJECTIVE: Interest has grown in combining radiology, pathology, genomic, and clinical data to improve the accuracy of diagnostic and prognostic predictions toward precision health. However, most existing works choose their datasets and modeling appr...

Optimizing clinical risk stratification of localized prostate cancer.

Current opinion in urology
PURPOSE OF REVIEW: To review the current risk and prognostic stratification systems in localised prostate cancer. To explore some of the most promising adjuncts to clinical models and what the evidence has shown regarding their value.

ChatGPT artificial intelligence in clinical data analysis: an example comparing standard fusion prostate biopsy outcomes after robotic-assisted radical prostatectomy (RaRP).

Archivio italiano di urologia, andrologia : organo ufficiale [di] Societa italiana di ecografia urologica e nefrologica
OBJECTIVE: To compare statistical outputs from ChatGPT 4.0 and human experts in both comparative and correlation analyses in the evaluation of multiparametric MRI/ultrasound fusion-targeted biopsy plus random biopsy versus standard random biopsy alon...

Validation and Derivation of miRNA-Based Germline Signatures Predicting Radiation Toxicity in Prostate Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Although radiotherapy (RT) is one of the primary treatment modalities used in the treatment of cancer, patients often experience toxicity during or after treatment. RT-induced genitourinary (GU) toxicity is a significant survivorship challen...