Notes

Neurorehabilitation regarding Parkinson's disease: Long term views pertaining to behavioral adaptation.
The aim of this single-center observational study was to evaluate the impact of implementing Enhanced Recovery After Surgery (ERAS) protocols, combined with systematic geriatric assessment and support, on surgical and oncological outcomes in patients aged 70 or older undergoing colonic cancer surgery.

Two groups were formed from an actively maintained database from all patients undergoing laparoscopic colonic surgery for neoplasms during a defined period before (standard group) or after (ERAS group) the introduction of an ERAS program associated with systematic geriatric assessment. The primary outcome was postoperative 90-day morbidity. Secondary outcomes were total length of hospital stay, initiated and completed adjuvant chemotherapy (AC) rate, and 1-year mortality rate.

A total of 266 patients (135 standard and 131 ERAS) were included in the study. Overall 90-day morbidity and mean hospital stay were significantly lower in the ERAS group than in the standard group (22.1% vs. 35.6%, p=0.02; and 6.2 vs. 9.3 days, p<0.01, respectively). There were no differences in readmission rates and anastomotic complications. AC was recommended in 114 patients. The rate of initiated treatment was comparable between the groups (66.6% vs. 77.7%, p=0.69). The rate of completed AC was significantly higher in the ERAS group (50% vs. 20%, p<0.01) with a lower toxicity rate (57.1% vs. learn more 87.5%, p=0.002). The 1-year mortality rate was higher in the standard group (7.4% vs. 0.8%, p<0.01).

The combination of ERAS protocols and geriatric assessment and support reduces the overall morbidity rate and improves 12-month oncologic outcomes.
The combination of ERAS protocols and geriatric assessment and support reduces the overall morbidity rate and improves 12-month oncologic outcomes.
Preoperative diagnosis of No.10 lymph nodes (LNs) metastases in advanced proximal gastric cancer (APGC) patients remains a challenge. The aim of this study was to develop a CT-based radiomics nomogram for identification of No.10 LNs status in APGCs.

A total of 515 patients with primary APGCs were retrospectively selected and divided into a training cohort (n=340) and a validation cohort (n=175). Total incidence of No.10 LNM was 12.4% (64/515). CT based radiomics nomogram combining with radiomic signature calculated from venous CT imaging features and CT-defined No.10 LNs status evaluated by radiologists was built and tested to predict the No.10 LNs status in APGCs.

CT based radiomics nomogram yielded classification accuracy with areas under ROC curves, AUC=0.896 and 0.814 in training and validation cohort, respectively, while radiomic signature and radiologist' diagnosis based on contrast-enhanced CT images yielded lower AUCs ranging in 0.742-0.866 and 0.619-0.685, respectively. In the specificity higher than 80%, the sensitivity of using radiomics nomogram, radiomic signature and radiologists' evaluation to detect No.10 LNs positive cases was 82.8% (53/64), 67.2% (43/64) and 39.1% (25/64), respectively.

The CT-based radiomics nomogram provides a promising and more effective method to yield high accuracy in identification of No.10 LNs metastases in APGC patients.
The CT-based radiomics nomogram provides a promising and more effective method to yield high accuracy in identification of No.10 LNs metastases in APGC patients.
Pressure ulcers are regions of trauma caused by a continuous pressure applied to soft tissues between a bony prominence and a hard surface. The manual monitoring of their healing evolution can be achieved by area assessment techniques that include the use of rulers and adhesive labels in direct contact with the injury, being highly inaccurate and subjective. In this paper we present a Support Vector Machine classifier in combination with a modified version of the GrabCut method for the automatic measurement of the area affected by pressure ulcers in digital images.

Three methods of region segmentation using the superpixel strategy were evaluated from which color and texture descriptors were extracted. After the superpixel classification, the GrabCut segmentation method was applied in order to delineate the region affected by the ulcer from the rest of the image.

Experiments on a set of 105 pressure ulcer images from a public data set resulted in an average accuracy of 96%, sensitivity of 94%, specificity of 97% and precision of 94%.

The association of support vector machines with superpixel segmentation outperformed current methods based on deep learning and may be extended to tissue classification.
The association of support vector machines with superpixel segmentation outperformed current methods based on deep learning and may be extended to tissue classification.
Augmented reality (AR) can help to overcome current limitations in computer assisted head and neck surgery by granting "X-ray vision" to physicians. Still, the acceptance of AR in clinical applications is limited by technical and clinical challenges. We aim to demonstrate the benefit of a marker-free, instant calibration AR system for head and neck cancer imaging, which we hypothesize to be acceptable and practical for clinical use.

We implemented a novel AR system for visualization of medical image data registered with the head or face of the patient prior to intervention. Our system allows the localization of head and neck carcinoma in relation to the outer anatomy. Our system does not require markers or stationary infrastructure, provides instant calibration and allows 2D and 3D multi-modal visualization for head and neck surgery planning via an AR head-mounted display. We evaluated our system in a pre-clinical user study with eleven medical experts.

Medical experts rated our application with a system usability scale score of 74.8 ± 15.9, which signifies above average, good usability and clinical acceptance. An average of 12.7 ± 6.6 minutes of training time was needed by physicians, before they were able to navigate the application without assistance.

Our AR system is characterized by a slim and easy setup, short training time and high usability and acceptance. Therefore, it presents a promising, novel tool for visualizing head and neck cancer imaging and pre-surgical localization of target structures.
Our AR system is characterized by a slim and easy setup, short training time and high usability and acceptance. Therefore, it presents a promising, novel tool for visualizing head and neck cancer imaging and pre-surgical localization of target structures.
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