High-dose stereotactic body radiotherapy using CyberKnife® for stage I peripheral lung cancer: a single-center retrospective study.

BACKGROUND: This retrospective study was performed to evaluate the efficacy and toxicity of high-dose stereotactic body radiotherapy (SBRT) using a CyberKnife® for patients with stage I peripheral non-small cell lung cancer (NSCLC). METHODS: Ninety-six patients with stage I peripheral NSCLC who were treated with SBRT using a CyberKnife® from August 2010 to June 2019 were identified and included in this study. Local control (LC), local progression-free survival (LPFS), progression-free survival (PFS), overall survival (OS), and late toxicity were evaluated. Potential risk factors associated with LC, LPFS, PFS, or OS were investigated by univariate analyses. RESULTS: Data of 96 patients were examined. The prescribed dose to the tumor was 54 Gy in 3 fractions in 91 patients and 60 Gy in 3 fractions in 5 patients. The median follow-up duration was 27 months. The 2-year LC, LPFS, PFS, and OS rates were 97%, 88%, 84%, and 90%, respectively. The T factor was significantly correlated with LC, LPFS, and PFS. The 2-year LC rate for patients with T1a/T1b and T1c/T2a disease was 100% and 90%, respectively (p < 0.05), and the 2-year PFS rate for the corresponding patients was 95% and 65%, respectively (p < 0.001). One patient (1%) developed grade 3 radiation pneumonitis. CONCLUSIONS: High-dose SBRT using a CyberKnife® for stage I peripheral NSCLC produced favorable treatment outcomes with acceptable late toxicity. Further studies are needed to improve the treatment outcomes for patients with T1c/T2a disease.

New methodology to obtain a calibration model for noninvasive near-infrared blood glucose monitoring.

This paper reports new methodology to obtain a calibration model for noninvasive blood glucose monitoring using diffuse reflectance near-infrared (NIR) spectroscopy. Conventional studies of noninvasive blood glucose monitoring with NIR spectroscopy use a calibration model developed by in vivo experimental data sets. In order to create a calibration model, we have used a numerical simulation of light propagation in skin tissue to obtain simulated NIR diffuse reflectance spectra. The numerical simulation method enables us to design parameters affecting the prediction of blood glucose levels and their variation ranges for a data set to create a calibration model using multivariate analysis without any in vivo experiments in advance. By designing the parameters and their variation ranges appropriately, we can prevent a calibration model from chance temporal correlations that are often observed in conventional studies using NIR spectroscopy. The calibration model (regression coefficient vector) obtained by the numerical simulation has a characteristic positive peak at the wavelength around 1600 nm. This characteristic feature of the regression coefficient vector is very similar to those obtained by our previous in vitro and in vivo experimental studies. This positive peak at around 1600 nm also corresponds to the characteristic absorption band of glucose. The present study has reinforced that the characteristic absorbance of glucose at around 1600 nm is useful to predict the blood glucose level by diffuse reflectance NIR spectroscopy. We have validated this new calibration methodology using in vivo experiments. As a result, we obtained a coefficient of determination, r2, of 0.87 and a standard error of prediction (SEP) of 12.3 mg/dL between the predicted blood glucose levels and the reference blood glucose levels for all the experiments we have conducted. These results of in vivo experiments indicate that if the parameters and their vibration ranges are appropriately taken into account in a numerical simulation, the new calibration methodology provides us with a very good calibration model that can predict blood glucose levels with small errors without conducting any experiments in advance to create a calibration model for each individual patient. This new calibration methodology using numerical simulation has promising potential for NIR spectroscopy, especially for noninvasive blood glucose monitoring.

Noninvasive near-infrared blood glucose monitoring using a calibration model built by a numerical simulation method: Trial application to patients in an intensive care unit.

We have applied a new methodology for noninvasive continuous blood glucose monitoring, proposed in our previous paper, to patients in ICU (intensive care unit), where strict controls of blood glucose levels are required. The new methodology can build calibration models essentially from numerical simulation, while the conventional methodology requires pre-experiments such as sugar tolerance tests, which are impossible to perform on ICU patients in most cases. The in vivo experiments in this study consisted of two stages, the first stage conducted on healthy subjects as preliminary experiments, and the second stage on ICU patients. The prediction performance of the first stage was obtained as a correlation coefficient (r) of 0.71 and standard error of prediction (SEP) of 28.7 mg/dL. Of the 323 total data, 71.5% were in the A zone, 28.5% were in the B zone, and none were in the C, D, and E zones for the Clarke error-grid analysis. The prediction performance of the second stage was obtained as an r of 0.97 and SEP of 27.2 mg/dL. Of the 304 total data, 80.3% were in the A zone, 19.7% were in the B zone, and none were in the C, D, and E zones. These prediction results suggest that the new methodology has the potential to realize a noninvasive blood glucose monitoring system using near-infrared spectroscopy (NIRS) in ICUs. Although the total performance of the present monitoring system has not yet reached a satisfactory level as a stand-alone system, it can be developed as a complementary system to the conventional one used in ICUs for routine blood glucose management, which checks the blood glucose levels of patients every few hours.