AIMC Topic: Retrospective Studies

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The correlation between serum creatinine and burn severity and its predictive value.

Cellular and molecular biology (Noisy-le-Grand, France)
This study aimed to explore the correlation between serum creatinine and burn severity and the value of predicting the outcome of patients. For this purpose, a total of 268 burn patients (BUP) were collected. According to the burn area, they were div...

Serological and Molecular Characterization of Occult HBV Infection in Blood Donors from South Italy.

Viruses
Despite good vaccine coverage and careful blood donor selection policies, hepatitis B virus (HBV) is still the most frequent viral infection among blood donors (BDs) in Italy, mostly in the occult form (OBI). We studied the virological features of OB...

Machine Learning and CT Texture Features in Ex-smokers with no CT Evidence of Emphysema and Mildly Abnormal Diffusing Capacity.

Academic radiology
RATIONALE AND OBJECTIVES: Ex-smokers without spirometry or CT evidence of chronic obstructive pulmonary disease (COPD) but with mildly abnormal diffusing capacity of the lungs for carbon monoxide (DL) are at higher risk of developing COPD. It remains...

The Effects of Daytime Variation on Short-term Outcomes of Patients Undergoing Off-Pump Coronary Artery Bypass Grafting.

Journal of cardiothoracic and vascular anesthesia
OBJECTIVE: To evaluate the effects of time of surgery on the short-term outcomes of patients undergoing off-pump coronary artery bypass grafting (OPCABG).

Ct-based subregional radiomics using hand-crafted and deep learning features for prediction of therapeutic response to anti-PD1 therapy in NSCLC.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To develop and externally validate subregional radiomics for predicting therapeutic response to anti-PD1 therapy in non-small-cell lung cancer (NSCLC).

Incorporation of quantitative imaging data using artificial intelligence improves risk prediction in veterans with liver disease.

Hepatology (Baltimore, Md.)
BACKGROUND AND AIMS: Utilization of electronic health records data to derive predictive indexes such as the electronic Child-Turcotte-Pugh (eCTP) Score can have significant utility in health care delivery. Within the records, CT scans contain phenoty...

Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep learning has presented great potential in accurate MR image segmentation when enough labeled data are provided for network optimization. However, manually annotating three-dimensional (3D) MR images is tedious and time-consuming, req...

AS-NeSt: A Novel 3D Deep Learning Model for Radiation Therapy Dose Distribution Prediction in Esophageal Cancer Treatment With Multiple Prescriptions.

International journal of radiation oncology, biology, physics
PURPOSE: Implementing artificial intelligence technologies allows for the accurate prediction of radiation therapy dose distributions, enhancing treatment planning efficiency. However, esophageal cancers present unique challenges because of tumor com...

Comparing fully automated AI body composition measures derived from thin and thick slice CT image data.

Abdominal radiology (New York)
PURPOSE: To compare fully automated artificial intelligence body composition measures derived from thin (1.25 mm) and thick (5 mm) slice abdominal CT data.