Prediction of SBRT response in liver cancer by combining original and delta cone-beam CT radiomics: a pilot study.

This study aims to explore the feasibility of utilizing a combination of original and delta cone-beam CT (CBCT) radiomics for predicting treatment response in liver tumors undergoing stereotactic body radiation therapy (SBRT). A total of 49 patients are included in this study, with 36 receiving 5-fraction SBRT, 3 receiving 4-fraction SBRT, and 10 receiving 3-fraction SBRT. The CBCT and planning CT images from liver cancer patients who underwent SBRT are collected to extract overall 547 radiomics features. The CBCT features which are reproducible and interchangeable with pCT are selected for modeling analysis. The delta features between fractions are calculated to depict tumor change. The patients with 4-fraction SBRT are only used for screening robust features. In patients receiving 5-fraction SBRT, the predictive ability of both original and delta CBCT features for two-level treatment response (local efficacy vs. local non-efficacy; complete response (CR) vs. partial response (PR)) is assessed by utilizing multivariable logistic regression with leave-one-out cross-validation. Additionally, univariate analysis is conducted to validate the capability of CBCT features in identifying local efficacy in patients receiving 3-fraction SBRT. In patients receiving 5-fraction SBRT, the combined models incorporating original and delta CBCT radiomics features demonstrate higher area under the curve (AUC) values compared to models using either original or delta features alone for both classification tasks. The AUC values for predicting local efficacy vs. local non-efficacy are 0.58 for original features, 0.82 for delta features, and 0.90 for combined features. For distinguishing PR from CR, the respective AUC values for original, delta and combined features are 0.79, 0.80, and 0.89. In patients receiving 3-fraction SBRT, eight valuable CBCT radiomics features are identified for predicting local efficacy. The combination of original and delta radiomics derived from fractionated CBCT images in liver cancer patients undergoing SBRT shows promise in providing comprehensive information for predicting treatment response.

Image domain multi-material decomposition using single energy CT.

Multi-material decomposition (MMD) technique decomposes the CT images into basis material images and has been promising in clinical practice for material composition quantification within the human body. MMD could be implemented using the image data acquired from spectral CT or its special case, dual-energy CT (DECT) while the spectral CT data acquisition usually requires a hardware modification. In this paper, we propose an image domain MMD method using single energy CT (SECT). The proposed objective function applies a least square data fidelity term to enforce the minimization between the linear combination of decomposed material image and the measured SECT image, and an edge-preserving (EP) regularization term to meet the piecewise constant property of the material image. We apply the optimization transfer principle to form a pixel-wise separable quadratic surrogate (PWSQS) function in each iteration to decrease the objective function. The pixelwise direct inversion method assisted by the two-material assumption (TMA) is applied to obtain a good initial value. The proposed method is evaluated using a digital phantom, a Catphan phantom and the clinical data. A low-pass filtration method is implemented for a comparison purpose. In the phantom study, the proposed TMA method achieves high volume fraction accuracy (VFA) of 79.64% and the proposed EP method further increases the VFA by 15.56% and decreases the decomposition standard deviation (STD) by 81.51% compared with the TMA method. At the comparable noise level, the proposed EP method increases spatial resolution by an overall factor of 1.01 when the modulation transfer function magnitude is decreased to 50% compared with the low-pass filtration method. In clinical data study, the virtual non-contrast image generated by the proposed method achieves the root-mean-squared-relative error of 2.93% compared with the contrast-free ground-truth image.

Scatter correction for a clinical cone-beam CT system using an optimized stationary beam blocker in a single scan.

PURPOSE: Scatter contamination in the cone-beam CT (CBCT) leads to CT number inaccuracy, spatial nonuniformity, and loss of image contrast. In our previous work, we proposed a single scan scatter correction approach using a stationary partial beam blocker. Although the previous method works effectively on a tabletop CBCT system, it fails to achieve high image quality on a clinical CBCT system mainly due to the wobble of the LINAC gantry during scan acquisition. Due to the mechanical deformation of CBCT gantry, the wobbling effect is observed in the clinical CBCT scan, and more missing data present using the previous blocker with the uniformly distributed lead strips. METHODS: An optimal blocker distribution is proposed to minimize the missing data. In the objective function of the missing data, the motion of the beam blocker in each projection is estimated using the segmentation due to its high contrast in the blocked area. The scatter signals from the blocker are also estimated using an air scan with the inserted blocker. The final image is generated using the forward projection to compensate for the missing data. RESULTS: On the Catphan©504 phantom, our approach reduces the average CT number error from 86 Hounsfield unit (HU) to 9 HU and improves the image contrast by a factor of 1.45 in the high-contrast rods. On a head patient, the CT number error is reduced from 97 HU to 6 HU in the soft-tissue region and the image spatial nonuniformity is decreased from 27% to 5%. CONCLUSIONS: The results suggest that the proposed method is promising for clinical applications.

A feedback constraint optimization method for intensity-modulated radiation therapy of nasopharyngeal carcinoma.

Intensity-modulated radiation therapy (IMRT) is able to achieve good target conformance with a limited dose to organs at risk (OARs); however, IMRT increases the irradiation volume and monitor units (MUs) required. The present study aimed to evaluate the use of an IMRT plan with fewer segments and MUs, while maintaining quality in the treatment of nasopharyngeal carcinoma. In the present study, two types of IMRT plan were therefore compared: The direct machine parameter optimization (DMPO)-RT method and the feedback constraint DMPO-RT (fc_DMPO-RT) method, which utilizes compensative feedback constraint in DMPO-RT and maintains optimization. Plans for 23 patients were developed with identical dose prescriptions. Each plan involved synchronous delivery to various targets, with identical OAR constraints, by means of 7 coplanar fields. The average dose, maximum dose, dose-volume histograms of targets and the OAR, MUs of the plan, the number of segments, delivery time and accuracy were subsequently compared. The fc_DMPO-RT exhibited superior dose distribution in terms of the average, maximum and minimum doses to the gross tumor volume compared with that of DMPO-RT (t=62.7, 20.5 and 22.0, respectively; P<0.05). The fc_DMPO-RT also resulted in a smaller maximum dose to the spinal cord (t=7.3; P<0.05), as well as fewer MUs, fewer segments and decreased treatment times than that of the DMPO-RT (t=6.2, 393.4 and 244.3, respectively; P<0.05). The fc_DMPO-RT maintained plan quality with fewer segments and MUs, and the treatment time was significantly reduced, thereby resulting in reduced radiation leakage and an enhanced curative effect. Therefore, introducing feedback constraint into DMPO may result in improved IMRT planning. In nasopharyngeal carcinoma specifically, feedback constraint resulted in the improved protection of OARs in proximity of targets (such as the brainstem and parotid) due to sharp dose distribution and reduced MUs.