AIMC Topic: Retrospective Studies

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Pulse Sequence Dependence of a Simple and Interpretable Deep Learning Method for Detection of Clinically Significant Prostate Cancer Using Multiparametric MRI.

Academic radiology
RATIONALE AND OBJECTIVES: Multiparametric magnetic resonance imaging (mpMRI) is increasingly used for risk stratification and localization of prostate cancer (PCa). Thanks to the great success of deep learning models in computer vision, the potential...

Accurate prediction of histological grading of intraductal papillary mucinous neoplasia using deep learning.

Endoscopy
BACKGROUND: Risk stratification and recommendation for surgery for intraductal papillary mucinous neoplasm (IPMN) are currently based on consensus guidelines. Risk stratification from presurgery histology is only potentially decisive owing to the low...

Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium.

The British journal of radiology
OBJECTIVE: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep-learning models in glioblastoma care pathways. Additionally, to understand the most common i...

The diagnostic and prognostic value of radiomics and deep learning technologies for patients with solid pulmonary nodules in chest CT images.

BMC cancer
BACKGROUND: Solid pulmonary nodules are different from subsolid nodules and the diagnosis is much more challenging. We intended to evaluate the diagnostic and prognostic value of radiomics and deep learning technologies for solid pulmonary nodules.

Financial Toxicity After Robot-Assisted Radical Prostatectomy and Its Relation with Oncologic, Functional Outcomes.

The Journal of urology
PURPOSE: The aim of the study was to evaluate frequency of financial toxicity among patients who underwent robot-assisted radical prostatectomy for prostate cancer.

Comparison of single-port and multi-port Retzius-sparing robot-assisted laparoscopic prostatectomy.

Journal of robotic surgery
Dissemination of robotic surgical technology for robot-assisted laparoscopic prostatectomy (RALP) has yielded advancements including the Retzius-sparing (RS) approach and the single-port (SP) platform. The safety and feasibility of each individual ad...

Measuring pure ground-glass nodules on computed tomography: assessing agreement between a commercially available deep learning algorithm and radiologists' readings.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Deep learning algorithms (DLAs) could enable automatic measurements of solid portions of mixed ground-glass nodules (mGGNs) in agreement with the invasive component sizes measured during pathologic examinations. However, the measurement o...

End-to-end deep learning model for segmentation and severity staging of anterior cruciate ligament injuries from MRI.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to develop a semi-supervised segmentation and classification deep learning model for the diagnosis of anterior cruciate ligament (ACL) tears on MRI based on a semi-supervised framework, double-linear layers U-Ne...

A Natural Language Processing and Machine Learning Approach to Identification of Incidental Radiology Findings in Trauma Patients Discharged from the Emergency Department.

Annals of emergency medicine
STUDY OBJECTIVE: Patients undergoing diagnostic imaging studies in the emergency department (ED) commonly have incidental findings, which may represent unrecognized serious medical conditions, including cancer. Recognition of incidental findings freq...

Successful implementation of a nurse-navigator-run program using natural language processing identifying patients with an abdominal aortic aneurysm.

Journal of vascular surgery
BACKGROUND: Abdominal aortic aneurysms (AAA) are often identified incidentally on imaging studies. Patients and/or providers are frequently unaware of these AAA and the need for long-term follow-up. We sought to evaluate the outcome of a nurse-naviga...