Is low or high body mass index in patients operated for oral squamous cell carcinoma associated with the perioperative complication rate?

The aim of this study was to analyse the effect of body mass index (BMI), both low and high values, on the perioperative complication rate in patients with oral squamous cell carcinoma (OSCC). The medical records of 259 patients operated between 2014 and 2017 for OSCC were reviewed. Univariate and multivariate analyses were performed. Sixty of the 259 patients developed 87 complications. Low or high BMI was not associated with the perioperative complication rate. A longer operating time and increased blood loss were associated with a higher perioperative complication rate and higher Clavien–Dindo grade. Low BMI, American Society of Anesthesiologists score 2 and 3, a longer operating time, and increased blood loss were associated with a longer hospital stay. Low BMI was associated with a longer hospital stay. Neither low nor high BMI was associated with the perioperative complication rate. A longer operating time and increased blood loss were associated with a higher perioperative complication rate and higher Clavien–Dindo grade.     

Comparison of three different types of splints and templates for maxilla repositioning in bimaxillary orthognathic surgery: a randomized controlled trial

The selection and implementation of a plan for maxillary surgery is of the utmost importance in achieving the desired outcome for the patient undergoing two-jaw orthognathic surgery. Some splint-based and splintless methods, accompanied by computer-assisted techniques, are helpful in improving surgical plan implementation. However, randomized controlled trials focused on this procedure are lacking. This study included 61 patients who underwent bimaxillary surgeries. The patients were randomly assigned to a conventional resin occlusal splint (CROS) group, a digital occlusal splint (DOS) group, or a digital templates (DT) group, in a 1:1:1 ratio. The mean linear distance between the planned and actual postoperative positions of eight selected points on the surfaces of the maxillary teeth was selected as the outcome measure. The distance was significantly smaller in the DT group (1.17 ± 0.66 mm) when compared to both the CROS group (2.55 ± 0.95 mm, P < 0.05) and DOS group (2.15 ± 1.12 mm, P < 0.05). However, the difference between the CROS group and DOS group was not statistically significant. These findings indicate that using digital templates results in the best performance in transferring the surgical plan to the operation environment as compared to the other two types of splints. This suggests that the application of digital templates could provide a reliable treatment option.     

A literature-based treatment algorithm for low-grade neuroendocrine liver metastases

BackgroundThe optimal timing of treatment of liver metastases from low-grade neuroendocrine tumors (LG-NELM) varies significantly due to numerous treatment modalities and the literature supporting various treatment(s). This study sought to create and validate a literature-based treatment algorithm for LG-NELM.MethodsA treatment algorithm to maximize overall survival (OS) was designed using peer-reviewed articles evaluating treatment of LG-NELM. This algorithm was retrospectively applied to patients treated for LG-NELM at our institution. Deviation was determined based on whether or not a patient received treatment consistent with that recommended by the algorithm. Patients who did and did not deviate from the algorithm were compared with respect to OS and number of treatments.ResultsApplying our algorithm to a 149-patient cohort, 57 (38%) deviated from recommended treatment. Deviation occurred in the form of alternative (28, 49%) versus additional procedures (29, 51%). Algorithm deviators underwent significantly more procedures than non-deviators (median 1 vs. 2, p < 0.001). Cox model indicated no difference in OS associated with algorithm deviation (HR 1.19, p = 0.58) when controlling for age and tumor characteristics.ConclusionThis literature-based algorithm helps standardize treatment protocols in patients with LG-NELM and can reduce cost and risk by minimizing unnecessary procedures. Prospective implementation and validation is required.     

Estimating Local Cellular Density in Glioma Using MR Imaging Data

BACKGROUND AND PURPOSE:Increased cellular density is a hallmark of gliomas, both in the bulk of the tumor and in areas of tumor infiltration into surrounding brain. Altered cellular density causes altered imaging findings, but the degree to which cellular density can be quantitatively estimated from imaging is unknown. The purpose of this study was to discover the best MR imaging and processing techniques to make quantitative and spatially specific estimates of cellular density.MATERIALS AND METHODS:We collected stereotactic biopsies in a prospective imaging clinical trial targeting untreated patients with gliomas at our institution undergoing their first resection. The data included preoperative MR imaging with conventional anatomic, diffusion, perfusion, and permeability sequences and quantitative histopathology on biopsy samples. We then used multiple machine learning methodologies to estimate cellular density using local intensity information from the MR images and quantitative cellular density measurements at the biopsy coordinates as the criterion standard.RESULTS:The random forest methodology estimated cellular density with R² = 0.59 between predicted and observed values using 4 input imaging sequences chosen from our full set of imaging data (T2, fractional anisotropy, CBF, and area under the curve from permeability imaging). Limiting input to conventional MR images (T1 pre- and postcontrast, T2, and FLAIR) yielded slightly degraded performance (R² = 0.52). Outputs were also reported as graphic maps.CONCLUSIONS:Cellular density can be estimated with moderate-to-strong correlations using MR imaging inputs. The random forest machine learning model provided the best estimates. These spatially specific estimates of cellular density will likely be useful in guiding both diagnosis and treatment.

Increased cellular density (CD) is a hallmark of cancer and a key feature in histologic glioma analysis.¹ Mapping cellular density throughout a tumor would be a valuable tool to probe how tumors infiltrate and analyze the transition between diseased and healthy brain. However, measuring CD requires tissue, which entails additional risks and is expensive to obtain. There is no currently accepted clinical algorithm to translate imaging data into quantitative assessments of CD.There is great need for a method to estimate CD noninvasively in human patients with gliomas. In this article, we describe the development of such a method using MR imaging data inputs by correlating with multiple biopsy specimens acquired during a prospective human clinical trial. We obtained comprehensive MR imaging, including conventional, diffusion, perfusion, and permeability imaging sequences. We used machine learning approaches to correlate imaging findings with CD measurements from pathology, devised an algorithm to estimate CD from MR imaging inputs, and generated CD maps for the visual display of the predictions. We identified the most informative imaging data subset. This work has multiple applications in the diagnosis and treatment of patients with gliomas: For example, the method can be used to guide biopsy, resection, and surgery and delineate tumor borderzones both pre- and postoperatively.²