AIMC Topic: Retrospective Studies

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Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI.

European radiology
OBJECTIVES: Prostate volume (PV) in combination with prostate specific antigen (PSA) yields PSA density which is an increasingly important biomarker. Calculating PV from MRI is a time-consuming, radiologist-dependent task. The aim of this study was t...

Ontology-based feature engineering in machine learning workflows for heterogeneous epilepsy patient records.

Scientific reports
Biomedical ontologies are widely used to harmonize heterogeneous data and integrate large volumes of clinical data from multiple sources. This study analyzed the utility of ontologies beyond their traditional roles, that is, in addressing a challengi...

Comparison of Traditional Radiomics, Deep Learning Radiomics and Fusion Methods for Axillary Lymph Node Metastasis Prediction in Breast Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: Accurate identification of axillary lymph node (ALN) status in breast cancer patients is important for determining treatment options and avoiding axillary overtreatments. Our study aims to comprehensively compare the perform...

Open retropubic radical prostatectomy: Still a well-established surgical technique for prostate cancer management.

Actas urologicas espanolas
INTRODUCTION: The surgical treatment options for prostate cancer have changed rapidly, given the expansion of robotics. However, open retropubic radical prostatectomy (ORP) will continue to be performed in areas with financial limitations or with lim...

Fully automated CT-based adiposity assessment: comparison of the L1 and L3 vertebral levels for opportunistic prediction.

Abdominal radiology (New York)
PURPOSE: The purpose of this study is to compare fully automated CT-based measures of adipose tissue at the L1 level versus the standard L3 level for predicting mortality, which would allow for use at both chest (L1) and abdominal (L3) CT.

Radiographic findings involved in knee osteoarthritis progression are associated with pain symptom frequency and baseline disease severity: a population-level analysis using deep learning.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
PURPOSE: To (1) develop a deep-learning (DL) algorithm capable of producing limb-length and knee-alignment measurements, and (2) determine the association between limb-length discrepancy (LLD), coronal-plane alignment, osteoarthritis (OA) severity, a...

A deep learning approach for automated diagnosis of pulmonary embolism on computed tomographic pulmonary angiography.

BMC medical imaging
BACKGROUND: Computed tomographic pulmonary angiography (CTPA) is the diagnostic standard for confirming pulmonary embolism (PE). Since PE is a life-threatening condition, early diagnosis and treatment are critical to avoid PE-associated morbidity and...

Shuffle-ResNet: Deep learning for predicting LGG IDH1 mutation from multicenter anatomical MRI sequences.

Biomedical physics & engineering express
The world health organization recommended to incorporate gene information such as isocitrate dehydrogenase 1 (IDH1) mutation status to improve prognosis, diagnosis, and treatment of the central nervous system tumors. We proposed our Shuffle Residual ...

Effect of Operative Time on Outcomes of Minimally Invasive Versus Open Pancreatoduodenectomy.

Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
OBJECTIVE: To evaluate how operative time interacts with outcomes among different approaches to pancreaticoduodenectomy (PD). Minimally invasive PDs (MIPD), which include laparoscopic (LPD) and robotic (RPD) approaches, are increasingly performed in ...