Efficacy of high-dose-rate brachytherapy with different radiation source activities among cervical cancer patients and risk factors for long-term outcomes: A 6-year retrospective study.

PURPOSE: This study aimed to assess the impact of dose rates due to natural decay of Iridium-192 sources and the risk factors of clinical outcomes for cervical cancer patients treated with high-dose-rate (HDR) brachytherapy. METHODS AND MATERIALS: Four ninety-four patients were divided into relatively-high-radioactive (rHR), relatively-medium-radioactive (rMR), and relatively-low-radioactive (rLR) groups for retrospective treatment response comparison. The short-term outcomes were evaluated using the 1-month /3-month follow-up results based on RECIST 1.1. Local recurrence-free survival (LRFS) and metastatic recurrence-free survival (MRFS) were selected as long-term outcomes. A class of transformation models with adaptive lasso was applied to assess the risk factors of long-term outcomes. RESULTS: No significant difference was identified in short- or long-term outcomes of different radioactive groups. Subgroup analyses demonstrated similar findings. In multivariate factor analysis, advanced stage was significantly associated with higher risk of local recurrence and metastatic recurrence (HR = 1.66, 95%confidence interval [CI] = 1.14-2.43, p = 0.008; HR = 1.57, 95%CI = 1.23-2.00, p < 0.001). Significant associations were observed between local recurrence and pathology, and between metastatic recurrence and pre-treatment serum indices, respectively (HR = 8.62, 95%CI = 2.28-32.60, p = 0.002; HR = 1.98, 95%CI=1.20-2.26, p = 0.008). CONCLUSIONS: Overall, there was no significant difference in long- or short-term efficacy of the HDR brachytherapy among the groups with different levels of activity of radiation sources. Stage, pathology, and pretreatment serum indices were crucial factors that affected the long-term outcomes.

Rational Design of Tunable Near-Infrared Oxazine Probe with Large Stokes Shift for Leucine Aminopeptidase Detection and Imaging.

Near-Infrared (NIR) fluorescence imaging with the advantages of deep tissue penetration and minimum background, has been widely employed and developed in the study of biological applications. However, small Stokes shifts, difficulty in optical tuning, and pH sensitivity are still the major limitations faced by current NIR dyes. To solve these problems, we rationally designed a pH insensitive amino-tunable NIR oxazine fluorophore DQF-NH2 , which exhibited large Stokes shift (125 nm) accompanied with NIR excitation/emission due to the introduction an asymmetrical alternating vibronic feature. By benefiting from the excellent photophysical properties of DQF-NH2 , we have successfully constructed the probe DQF-NH2 -LAP with the ability to detect endogenous LAP. Bioimaging assays demonstrated that DQF-NH2 -LAP can not only effectively detect LAP in living cells, but also was successfully applied to image tumor tissue in vivo. We anticipate that the functionalizable dye DQF-NH2 may be a potential new NIR dye platform with an optically tunable group for the development of future desirable probes for bioimaging.

Prognostic Factors Analysis of Metastatic Recurrence in Cervical Carcinoma Patients Treated with Definitive Radiotherapy: A Retrospective Study Using Mixture Cure Model.

OBJECTIVES: This study aims to identify prognostic factors associated with metastatic recurrence-free survival of cervical carcinoma (CC) patients treated with radical radiotherapy and assess the cure probability of radical radiotherapy from metastatic recurrence. METHODS: Data were from 446 cervical carcinoma patients with radical radiotherapy for an average follow up of 3.96 years. We applied a mixture cure model to investigate the association between metastatic recurrence and prognostic factors and the association between noncure probability and factors, respectively. A nonparametric test of cure probability under the framework of a mixture cure model was used to examine the significance of cure probability of the definitive radiotherapy treatment. Propensity-score-matched (PSM) pairs were generated to reduce bias in subgroup analysis. RESULTS: Patients in advanced stages (p = 0.005) and those with worse treatment responses in the 3rd month (p = 0.004) had higher metastatic recurrence rates. Nonparametric tests of the cure probability showed that 3-year cure probability from metastatic recurrence was significantly larger than 0, and 5-year cure probability was significantly larger than 0.7 but no larger than 0.8. The empirical cure probability by mixture cure model was 79.2% (95% CI: 78.6-79.9%) for the entire study population, and the overall median metastatic recurrence time for uncured patients (patients susceptible to metastatic recurrence) was 1.60 (95% CI: 1.51-1.69) years. Locally advanced/advanced stage was a risk factor but non-significant against the cure probability (OR = 1.078, p = 0.088). The interaction of age and activity of radioactive source were statistically significant in the incidence model (OR = 0.839, p = 0.025). In subgroup analysis, compared with high activity of radioactive source (HARS), low activity of radioactive source (LARS) significantly contributed to a 16.1% higher cure probability for patients greater than 53 years old, while cure probability was 12.2% lower for the younger patients. CONCLUSIONS: There was statistically significant evidence in the data showing the existence of a large amount of patients cured by the definitive radiotherapy treatment. HARS is a protective factor against metastatic recurrence for uncured patients, and young patients tend to benefit more than the elderly from the HARS treatment.

Simulation of a new respiratory phase sorting method for 4D-imaging using optical surface information towards precision radiotherapy.

BACKGROUND: Respiratory signal detection is critical for 4-dimensional (4D) imaging. This study proposes and evaluates a novel phase sorting method using optical surface imaging (OSI), aiming to improve the precision of radiotherapy. METHOD: Based on 4D Extended Cardiac-Torso (XCAT) digital phantom, OSI in point cloud format was generated from the body segmentation, and image projections were simulated using the geometries of Varian 4D kV cone-beam-CT (CBCT). Respiratory signals were extracted respectively from the segmented diaphragm image (reference method) and OSI respectively, where Gaussian Mixture Model and Principal Component Analysis (PCA) were used for image registration and dimension reduction respectively. Breathing frequencies were compared using Fast-Fourier-Transform. Consistency of 4DCBCT images reconstructed using Maximum Likelihood Expectation Maximization algorithm was also evaluated quantitatively, where high consistency can be suggested by lower Root-Mean-Square-Error (RMSE), Structural-Similarity-Index (SSIM) value closer to 1, and larger Peak-Signal-To-Noise-Ratio (PSNR) respectively. RESULTS: High consistency of breathing frequencies was observed between the diaphragm-based (0.232 Hz) and OSI-based (0.251 Hz) signals, with a slight discrepancy of 0.019Hz. Using end of expiration (EOE) and end of inspiration (EOI) phases as examples, the mean±1SD values of the 80 transverse, 100 coronal and 120 sagittal planes were 0.967, 0,972, 0.974 (SSIM); 1.657 ± 0.368, 1.464 ± 0.104, 1.479 ± 0.297 (RMSE); and 40.501 ± 1.737, 41.532 ± 1.464, 41.553 ± 1.910 (PSNR) for the EOE; and 0.969, 0.973, 0.973 (SSIM); 1.686 ± 0.278, 1.422 ± 0.089, 1.489 ± 0.238 (RMSE); and 40.535 ± 1.539, 41.605 ± 0.534, 41.401 ± 1.496 (PSNR) for EOI respectively. CONCLUSIONS: This work proposed and evaluated a novel respiratory phase sorting approach for 4D imaging using optical surface signals, which can potentially be applied to precision radiotherapy. Its potential advantages were non-ionizing, non-invasive, non-contact, and more compatible with various anatomic regions and treatment/imaging systems.

Comprehensive prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma.

Purpose: To propose and evaluate a comprehensive modeling approach combing radiomics, dosiomics and clinical components, for more accurate prediction of locoregional recurrence risk after radiotherapy for patients with locoregionally advanced HPSCC. Materials and methods: Clinical data of 77 HPSCC patients were retrospectively investigated, whose median follow-up duration was 23.27 (4.83-81.40) months. From the planning CT and dose distribution, 1321 radiomics and dosiomics features were extracted respectively from planning gross tumor volume (PGTV) region each patient. After stability test, feature dimension was further reduced by Principal Component Analysis (PCA), yielding Radiomic and Dosiomic Principal Components (RPCs and DPCs) respectively. Multiple Cox regression models were constructed using various combinations of RPC, DPC and clinical variables as the predictors. Akaike information criterion (AIC) and C-index were used to evaluate the performance of Cox regression models. Results: PCA was performed on 338 radiomic and 873 dosiomic features that were tested as stable (ICC1 > 0.7 and ICC2 > 0.95), yielding 5 RPCs and DPCs respectively. Three comprehensive features (RPC0, P<0.01, DPC0, P<0.01 and DPC3, P<0.05) were found to be significant in the individual Radiomic or Dosiomic Cox regression models. The model combining the above features and clinical variable (total stage IVB) provided best risk stratification of locoregional recurrence (C-index, 0.815; 95%CI, 0.770-0.859) and prevailing balance between predictive accuracy and complexity (AIC, 143.65) than any other investigated models using either single factors or two combined components. Conclusion: This study provided quantitative tools and additional evidence for the personalized treatment selection and protocol optimization for HPSCC, a relatively rare cancer. By combining complementary information from radiomics, dosiomics, and clinical variables, the proposed comprehensive model provided more accurate prediction of locoregional recurrence risk after radiotherapy.

Deep learning-based synthetization of real-time in-treatment 4D images using surface motion and pretreatment images: A proof-of-concept study.

PURPOSE: To develop a deep learning model that maps body surface motion to internal anatomy deformation, which is potentially applicable to dose-free real-time 4D virtual image-guided radiotherapy based on skin surface data. METHODS: Body contours were segmented out of 4DCT images. Deformable image registration algorithm was used to register the end-of-exhalation (EOE) phase to other phases. Deformation vector field was dimension-reduced to the first two principal components (PCs). A deep learning model was trained to predict the two PC scores of each phase from surface displacement. The instant deformation field can then be reconstructed, warping EOE image to obtain real-time CT image. This approach was validated on 4D XCAT phantom, the public DIR-Lab, and 4D-Lung dataset respectively, with and without simulated noise. RESULTS: Validation accuracy of the tumor centroid trajectory was observed as 0.04 ± 0.02 mm on XCAT phantom. For the DIR-Lab dataset, 300 landmarks were annotated on the end-of-inhalation (EOI) images of each patient, and the mean displacements between their predicted and reference positions were below 2 mm for all studied cases. For the 4D-Lung dataset, the average dice coefficients ± std between predicted and reference tumor contours at EOI phase were 0.835 ± 0.092 for all studied cases. CONCLUSIONS: A deep learning-based approach was proposed and validated to predict internal anatomy deformation from the surface motion, which is potentially applicable to on-line target navigation for accurate radiotherapy based on real-time 4D skin surface data and pretreatment images.