Explore the vast range of titles available.

Background To evaluate spot-scanning proton arc therapy (SPArc) and multi-field robust optimized intensity modulated proton therapy (RO-IMPT) in treating stage III non-small-cell lung cancer (NSCLC) patients. Methods Two groups of stage IIIA or IIIB NSCLC patients (group 1: eight patients with tumor motion less than 5 mm; group 2: six patients with tumor motion equal to or more than 5 mm) were re-planned with SPArc and RO-IMPT. Both plans were generated using robust optimization to achieve an optimal coverage with 99% of internal target volume (ITV) receiving 66 Gy (RBE) in 33 fractions. The dosimetric results and plan robustness were compared for both groups. The interplay effect was evaluated based on the ITV coverage by single-fraction 4D dynamic dose. Total delivery time was simulated based on a full gantry rotation with energy-layer-switching-time (ELST) from 0.2 to 4 s. Statistical analysis was also evaluated via Wilcoxon signed rank test. Results Both SPArc and RO-IMPT plans achieved similar robust target volume coverage for all patients, while SPArc significantly reduced the doses to critical structures as well as the interplay effect. Specifically, compared to RO-IMPT, SPArc reduced the average integral dose by 7.4% ( p  = 0.001), V 20 , and mean lung dose by an average of 3.2% ( p  = 0.001) and 1.6 Gy (RBE) ( p  = 0.001), the max dose to cord by 4.6 Gy (RBE) ( p  = 0.04), and the mean dose to heart and esophagus by 0.7 Gy (RBE) ( p  = 0.01) and 1.7 Gy (RBE) ( p  = 0.003) respectively. The average total estimated delivery time was 160.1 s, 213.8 s, 303.4 s, 840.8 s based on ELST of 0.2 s, 0.5 s, 1 s, and 4 s for SPArc plans, compared with the respective values of 182.0 s ( p  = 0.001), 207.9 s ( p  = 0.22), 250.9 s ( p  = 0.001), 509.4 s ( p  = 0.001) for RO-IMPT plans. Hence, SPArc plans could be clinically feasible when using a shorter ELST. Conclusions This study has indicated that SPArc could further improve the dosimetric results in patients with locally advanced stage NSCLC and potentially be implemented into routine clinical practice.

Endogeneity is a potential anomaly in econometric models, which may cause inconsistent parameter estimates. Transport models are prone to this problem and applications that properly correct for it are scarce. This paper focuses on how to address this issue in the case of strategic urban mode choice models (i.e., the third stage of classic strategic transport models), possibly the main tool for the assessment of costly transport projects. To address this problem, we propose and validate, for the first time, adequate instruments that may be obtained from data that is already available in this context. The proposed method is implemented using the Control Function approach, which we use to detect and correct for endogeneity in a case study in Valparaiso, Chile. The effects arising from the neglected endogeneity in this case study reflect on an overestimation between 26–49% of the subjective value of time and an underestimation of 33–75% of modal elasticities.

Purpose Segmentation of organs‐at‐risk (OARs) is an essential component of the radiation oncology workflow. Commonly segmented thoracic OARs include the heart, esophagus, spinal cord, and lungs. This study evaluated a convolutional neural network (CNN) for automatic segmentation of these OARs. Methods The dataset was created retrospectively from consecutive radiotherapy plans containing all five OARs of interest, including 22,411 CT slices from 168 patients. Patients were divided into training, validation, and test datasets according to a 66%/17%/17% split. We trained a modified U‐Net, applying transfer learning from a VGG16 image classification model trained on ImageNet. The Dice coefficient and 95% Hausdorff distance on the test set for each organ was compared to a commercial atlas‐based segmentation model using the Wilcoxon signed‐rank test. Results On the test dataset, the median Dice coefficients for the CNN model vs. the multi‐atlas model were 71% vs. 67% for the spinal cord, 96% vs. 94% for the right lung, 96%vs. 94% for the left lung, 91% vs. 85% for the heart, and 63% vs. 37% for the esophagus. The median 95% Hausdorff distances were 9.5  mm vs. 25.3 mm, 5.1  mm vs. 8.1 mm, 4.0  mm vs. 8.0 mm, 9.8  mm vs. 15.8 mm, and 9.2 mm vs. 20.0 mm for the respective organs. The results all favored the CNN model (P < 0.05). Conclusions A 2D CNN can achieve superior results to commercial atlas‐based software for OAR segmentation utilizing non‐domain transfer learning, which has potential utility for quality assurance and expediting patient care.

To monitor delivered dose and trigger plan adaptation when deviation becomes unacceptable, a clinical treatment dose (Tx‐Dose) reconstruction system based on three‐dimensional (3D)/four‐dimensional (4D)‐cone beam computed tomograpy (CBCT) images was developed and evaluated on various treatment sites, particularly for lung cancer patient treated by stereotactic body radiation therapy (SBRT). This system integrates with our treatment planning system (TPS), Linacs recording and verification system (R&V), and CBCT imaging system, consisting of three modules: Treatment Schedule Monitoring module (TSM), pseudo‐CT Generating module (PCG), and Treatment Dose Reconstruction/evaluation module (TDR). TSM watches the treatment progress in the R&V system and triggers the PCG module when new CBCT is available. PCG retrieves the CBCTs and performs planning CT to CBCT deformable registration (DIR) to generate pseudo‐CT. The 4D‐CBCT images are taken for target localization and correction in lung cancer patient before treatment. To take full advantage of the valuable information carried by 4D‐CBCT, a novel phase‐matching DIR scheme was developed to generate 4D pseudo‐CT images for 4D dose reconstruction. Finally, TDR module creates TPS scripts to automate Tx‐Dose calculation on the pseudo‐CT images. Both initial quantitative commissioning and patient‐specific qualitative quality assurance of the DIR tool were utilized to ensure the DIR quality. The treatment doses of ten patients (six SBRT‐lung, two head and neck (HN), one breast and one prostate cancer patients) were retrospectively constructed and evaluated. The target registration error (mean ± STD: 1.05 ± 1.13 mm) of the DIR tool is comparable to the interobserver uncertainty (0.88 ± 1.31 mm) evaluated by a publically available lung‐landmarks dataset. For lung SBRT patients, the D99 of the final cumulative Tx‐Dose of GTV is 93.8 ± 5.5% (83.7–100.1%) of the originally planned D99. CTV D99 decreases by 3% and mean ipsilateral parotid dose increases by 11.5% for one of the two HN patients. In conclusion, we have demonstrated the feasibility and effectiveness of a treatment dose verification system in our clinical setting.

Background and objective Radiation pneumonitis (RP) could be a lethal complication of lung cancer treatment. No reliable predictors of RP severity have been recognized. This prospective pilot study was performed to identify early predictors of high grade lung toxicity and to evaluate clinical, biological or dosimetric features associated with different grades of toxicity. Method Sixteen patients with non-small cell lung cancer with indication of concurrent chemoradiotherapy using 60 Gy/2 Gy/fraction starting at cycle one of platinum based chemotherapy were included. Bronchoalveolar lavage (BAL), pulmonary function testing (PFT), and 18 F-2-fluoro-2-deoxy-D-glucose positron-emission tomography was performed before radiotherapy (RT), after three weeks of treatment, and two months post-RT. For analysis, patients were grouped by grade (low [G1-G2] vs. high [G3-G5]). The two groups were compared to identify predictors of RP. Protein expression BAL and lung tissue metabolism was evaluated in two patients (RP-G1 vs. RP-G3). Categorical variables such as comorbidities, stages and locations were summarized as percentages. Radiation doses, pulmonary function values and time to RP were summarized by medians with ranges or as means with standard deviation. Longitudinal analysis PFT was performed by a T-test. Results All 16 patients developed RP, as follows: G1 (5 pts; 31.3%); G2 (5 pts; 31.3%); G3 (5 pts; 31.3%); and G5 (1 pts; 6.1%). Patients with high grade RP presented significant decrease ( p  = 0.02) in diffusing lung capacity for carbon monoxide (DLCO) after three weeks of RT. No correlation between dosimetric values and RP grades was observed. BAL analysis of the selected patients showed that CXCL-1, CD154, IL-1ra, IL-23, MIF, PAI-1 and IFN-γ were overexpressed in the lungs of the RP-G3 patient, even before treatment. The pre-RT SUVmax value in the RP-G3 patient was non-significantly higher than in the patient with RP-G1. Conclusions RT induces some degree of RP. Our data suggest that decrease in DLCO% is the most sensitive parameter for the early detection of RP. Moreover, we detect biological differences between the two grades of pneumonitis, highlighting the potential value of some cytokines as a prognostic marker for developing high grade lung toxicity. Further multicenter studies with larger sample size are essential to validate these findings.

Introduction 4DCT‐ventilation is an exciting new imaging modality that uses 4DCT data to calculate lung‐function maps. Because 4DCTs are acquired as standard of care for lung cancer patients undergoing radiotherapy, 4DCT‐ventiltation provides functional information at no extra dosimetric or monetary cost to the patient. The development of clinical trials is underway to use 4DCT‐ventilation imaging to spare functional lung in patients undergoing radiotherapy. The purpose of this work was to perform a virtual trial using retrospective data to develop the practical aspects of a 4DCT‐ventilation functional avoidance clinical trial. Methods The study included 96 stage III lung cancer patients. A 4DCT‐ventilation map was calculated using the patient's 4DCT‐imaging, deformable registration, and a density‐change‐based algorithm. Clinical trial inclusion assessment used quantitative and qualitative metrics based on the patient's spatial ventilation profile. Clinical and functional plans were generated for 25 patients. The functional plan aimed to reduce dose to functional lung while meeting standard target and critical structure constraints. Standard and dose‐function metrics were compared between the clinical and functional plans. Results Our data showed that 69% and 59% of stage III patients have regional variability in function based on qualitative and quantitative metrics, respectively. Functional planning demonstrated an average reduction of 2.8 Gy (maximum 8.2 Gy) in the mean dose to functional lung. Conclusions Our work demonstrated that 60–70% of stage III patients would be eligible for functional planning and that a typical functional lung mean dose reduction of 2.8 Gy can be expected relative to standard clinical plans. These findings provide salient data for the development of functional clinical trials.

The accurate identification of dangerous areas with high accident rates allowing governmental agencies responsible for improving road safety to properly allocate investment in critically accident prone road sections. Given this immediate need, this study aims to determine which segments are prone to accidents as well as the development of a hazard ranking of the accident prone road sections located within the city limits of Ocaña, Colombia, through the use of quantile regression. Based on the estimated model corresponding to quantile 95, it was possible to establish causal relationships between the frequency of accidents and characteristics such as length of the road section, width of the roadway, number of lanes, number of intersections, average daily traffic and average speed. The results indicate a total of seven accident prone road sections, for which a hazard ranking was established.

Multiple myeloma is the most common plasma cell dyscrasia and causes 2% of all cancer deaths in Western countries. Ovarian carcinosarcomas are very rare gynecological malignancies and account for only 1–2% of all ovarian tumors. In this case, we report a 67-year-old woman with known relapsed ovarian carcinosarcoma who presented with headache and neck pain. She was found to have new lytic lesions in the cranial and thoracic regions. While these lesions were assumed to be metastases, a diligent approach detected an M-spike on serum protein electrophoresis and a monoclonal gammopathy with immunoglobulin G lambda monoclonal immunoglobulin on immunofixation. A bone marrow biopsy confirmed the diagnosis of multiple myeloma. To our knowledge, this is the first ever reported case of concomitant multiple myeloma and ovarian carcinosarcoma. Our case highlights the utmost importance of a systematic approach to lytic lesions and emphasizes the need to consider secondary malignancies in the evaluation of possible metastases. We used the International Myeloma Working Group guidelines for screening and diagnosing multiple myeloma, and we provide a thorough review of this updated approach.

Purpose In our conventional image registration workflow, the four‐dimensional (4D) CBCT was directly registered to the reference helical CT (HCT) using a dual registration approach within the Elekta XVI software. In this study, we proposed a new HCT–CBCT auto‐registration strategy using a previously registered CBCT (CBCTpre) as the reference image and tested its clinical feasibility. Methods From a previous CBCT session, the registered average 4D CBCT was selected as CBCTpre and the HCT–CBCTpre registration vector from the clinician's manual registration result was recorded. In the new CBCT session, auto‐registration was performed between the new average 4D CBCT (CBCTtx) and CBCTpre (CBCTpre‐CBCTtx). The overall HCT–CBCTtx registration result was then derived by combing the results from two registrations (i.e., HCT–CBCTpre + CBCTpre–CBCTtx). The results from the proposed method were compared with clinician's manually adjusted HCT–CBCTtx registration results (“ground truth”) to evaluate its accuracy using a test dataset consisting of 32 challenging registration cases. Results The uncertainty of the proposed auto‐registration method was −0.1 ± 0.5, 0.1 ± 1.0, and −0.1 ± 0.7 mm in three translational directions (lateral, longitudinal, and vertical) and 0.0° ± 0.9°, 0.3° ± 0.9°, and 0.4° ± 0.7° in three rotation directions, respectively. Two patients (6.3%) had translational uncertainty > 2 mm (max = 3.1 mm) and both occurred in the longitudinal direction. Meanwhile, the uncertainty of the conventional direct HCT–CBCTtx auto‐registration was −0.4 ± 2.6, −0.2 ± 7.4, −1.4 ± 3.6 mm for translations and −0.3° ± 1.2°, 0.0° ± 1.6°, and 0.1 ± 1.1° for rotations. Eleven patients (34.4%) had translation uncertainty > 2 mm (max = 26.2 mm) in at least one direction. Accuracy in translation was improved with the new method, while rotation accuracy stayed in the same order. Conclusion We demonstrated the feasibility of incorporating prior clinical registration knowledge into the online HCT–CBCT registration process. The proposed auto‐registration method provides a quick and reliable starting solution for online HCT–CBCT registration.

Purpose Clinical image pairs provide the most realistic test data for image registration evaluation. However, the optimal registration is unknown. Using combinatorial rigid registration optimization (CORRO) we demonstrate a method to estimate the optimal alignment for rigid‐registration of clinical image pairs. Methods Expert selected landmark pairs were selected for each CT/CBCT image pair for six cases representing head and neck, thoracic, and pelvic anatomic regions. Combination subsets of a k number of landmark pairs (k‐combination set) were generated without repeat to form a large set of k‐combination sets (k‐set) for k = 4,8,12. The rigid transformation between the image pairs was calculated for each k‐combination set. The mean and standard deviation of these transformations were used to derive final registration for each k‐set. Results The standard deviation of registration output decreased as the k‐size increased for all cases. The joint entropy evaluated for each k‐set of each case was smaller than those from two commercially available registration programs indicating a stronger correlation between the image pair after CORRO was used. A joint histogram plot of all three algorithms showed high correlation between them. As further proof of the efficacy of CORRO the joint entropy of each member of 30 000 k‐combination sets in k = 4 were calculated for one of the thoracic cases. The minimum joint entropy was found to exist at the estimated mean of registration indicating CORRO converges to the optimal rigid‐registration results. Conclusions We have developed a methodology called CORRO that allows us to estimate optimal alignment for rigid‐registration of clinical image pairs using a large set landmark point. The results for the rigid‐body registration have been shown to be comparable to results from commercially available algorithms for all six cases. CORRO can serve as an excellent tool that can be used to test and validate rigid registration algorithms.