AIMC Topic: Prostatic Neoplasms

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A hierarchical deep reinforcement learning framework for intelligent automatic treatment planning of prostate cancer intensity modulated radiation therapy.

Physics in medicine and biology
We have previously proposed an intelligent automatic treatment planning (IATP) framework that builds a virtual treatment planner network (VTPN) to operate a treatment planning system (TPS) to generate high-quality radiation therapy (RT) treatment pla...

A risk grouping algorithm for predicting factors of persistently elevated prostate-specific antigen in patients following robot-assisted radical prostatectomy.

International journal of clinical practice
OBJECTIVE: After radical prostatectomy, prostate-specific antigen(PSA) value measuring ≥0.1 ng/mL is defined as persistent PSA(pPSA) and in many studies, it was found to be associated with aggressive disease and poor prognosis. Our aim in this study ...

The clinical impact of robot-assisted laparoscopic rectal cancer surgery associated with robot-assisted radical prostatectomy.

Asian journal of endoscopic surgery
INTRODUCTION: Robot-assisted laparoscopic surgery has been performed in various fields, especially in the pelvic cavity. However, little is known about the utility of robot-assisted laparoscopic rectal cancer surgery associated with robot-assisted ra...

Severe intraoperative bleeding predicts the risk of perioperative blood transfusion after robot-assisted radical prostatectomy.

Journal of robotic surgery
To evaluate potential factors associated with the risk of perioperative blood transfusion (PBT) with implications on length of hospital stay (LOHS) and major post-operative complications in patients who underwent robot-assisted radical prostatectomy ...

Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer.

Journal of clinical pathology
Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly and delayed decision-making in routine oncology care and treatment. As cancer is expected to rank a...

Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers.

Radiation oncology (London, England)
PURPOSE: We recently described the validation of deep learning-based auto-segmented contour (DC) models for organs at risk (OAR) and clinical target volumes (CTV). In this study, we evaluate the performance of implemented DC models in the clinical ra...

Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer.

Nature medicine
Machine learning (ML) holds great promise for impacting healthcare delivery; however, to date most methods are tested in 'simulated' environments that cannot recapitulate factors influencing real-world clinical practice. We prospectively deployed and...

Domain adaptation for segmentation of critical structures for prostate cancer therapy.

Scientific reports
Preoperative assessment of the proximity of critical structures to the tumors is crucial in avoiding unnecessary damage during prostate cancer treatment. A patient-specific 3D anatomical model of those structures, namely the neurovascular bundles (NV...