Treatment Reproducibility in Brain Stereotactic Radiotherapy Using a Shim Mask Versus a Standard Mask.

Introduction This study aimed to evaluate the setup accuracy of the new shim mask with mouth bite compared to the standard full brain mask in stereotactic radiosurgery (SRS) and radiotherapy (SRT) treatments for brain metastases or tumors. Method A combined retrospective and prospective design was employed, involving 40 patients treated at our center. Patients previously treated using standard head masks formed the retrospective cohort, while those treated with the Shim mask and mouth bite formed the prospective cohort. Daily cone-beam computed tomography (CBCT) scans were obtained before each treatment session to ensure patient setup accuracy. Key metrics included absolute shifts in translational and rotational directions, the number of repeat CBCTs, and the time interval between CBCTs. Results The Shim mask significantly reduced the mean setup errors in the lateral translation (p=0.022) from 0.17 cm (SD=0.10) to 0.10 cm (SD=0.10), and in X-axis rotation (p=0.030) from 0.79° (SD=0.43) to 0.47° (SD=0.47). By considering cutoff points of 1 mm in translational and 1° in rotational directions, the Shim mask was significantly more accurate in the lateral direction (p=0.004). Moreover, while 70% of patients in the standard group required repeat CBCT scans, none in the Shim group did, resulting in an average time saving of 10.4 minutes per patient. Conclusion The Shim mask with mouth bite offers enhanced immobilization accuracy in SRT/SRS treatments, leading to time and potential cost savings by reducing the need for repeat CBCT scans. This underscores the importance of adopting innovative immobilization techniques to optimize patient outcomes.

Knowledge- and Experience-Based Perceptions of Radiation Therapists during the COVID-19 Outbreak.

Objective: To assess the perceived risks and impact of the COVID-19 outbreak on radiation therapists in Saudi Arabia. Methods: A questionnaire was distributed to all radiation therapists in the country. The questionnaire contained questions about demographic characteristics, the extent of the pandemic's impact on hospital resources, risk perception, work-life, leadership, and immediate supervision. The questionnaire's reliability was assessed using Cronbach's alpha; >0.7 was considered adequate. Results: Out of the 127 registered radiation therapists, 77 (60.6%) responded; 49 (63.6%) females; and 28 (36.4%) males. The mean age was 36.8 ± 12.5 years. Nine (12%) of the participants had a past experience with pandemics or epidemics. Further, 46 (59.7%) respondents correctly identified the mode of transmission of COVID-19. Approximately, 69% of the respondents perceived COVID-19 as more than a minor risk to their families and 63% to themselves. COVID-19 had an overall negative impact on work at the personal and organizational levels. However, there was a positive attitude toward organizational management during the pandemic in general; positive responses ranged from 66.2% to 82.4%. Ninety-two percent considered protective resources and 70% considered the availability of supportive staff to be adequate. Demographic characteristics were not significantly associated with the perceived risk. Conclusions: Despite the high perception of risk and negative impact on their work, radiation therapists conveyed a positive overall perception regarding resource availability, supervision, and leadership. Efforts should be made to improve their knowledge and appreciate their efforts.

Personalized Liver Cancer Risk Prediction Using Big Data Analytics Techniques with Image Processing Segmentation.

A technology known as data analytics is a massively parallel processing approach that may be used to forecast a wide range of illnesses. Many scientific research methodologies have the problem of requiring a significant amount of time and processing effort, which has a negative impact on the overall performance of the system. Virtual screening (VS) is a drug discovery approach that makes use of big data techniques and is based on the concept of virtual screening. This approach is utilised for the development of novel drugs, and it is a time-consuming procedure that includes the docking of ligands in several databases in order to build the protein receptor. The proposed work is divided into two modules: image processing-based cancer segmentation and analysis using extracted features using big data analytics, and cancer segmentation and analysis using extracted features using image processing. This statistical approach is critical in the development of new drugs for the treatment of liver cancer. Machine learning methods were utilised in the prediction of liver cancer, including the MapReduce and Mahout algorithms, which were used to prefilter the set of ligand filaments before they were used in the prediction of liver cancer. This work proposes the SMRF algorithm, an improved scalable random forest algorithm built on the MapReduce foundation. Using a computer cluster or cloud computing environment, this new method categorises massive datasets. With SMRF, small amounts of data are processed and optimised over a large number of computers, allowing for the highest possible throughput. When compared to the standard random forest method, the testing findings reveal that the SMRF algorithm exhibits the same level of accuracy deterioration but exhibits superior overall performance. The accuracy range of 80 percent using the performance metrics analysis is included in the actual formulation of the medicine that is utilised for liver cancer prediction in this study.

Invasive growth associated with cold-inducible RNA-binding protein expression drives recurrence of surgically resected brain metastases.

BACKGROUND: Sixty percent of surgically resected brain metastases (BrM) recur within 1 year. These recurrences have long been thought to result from the dispersion of cancer cells during surgery. We tested the alternative hypothesis that invasion of cancer cells into the adjacent brain plays a significant role in local recurrence and shortened overall survival. METHODS: We determined the invasion pattern of 164 surgically resected BrM and correlated with local recurrence and overall survival. We performed single-cell RNA sequencing (scRNAseq) of >15,000 cells from BrM and adjacent brain tissue. Validation of targets was performed with a novel cohort of BrM patient-derived xenografts (PDX) and patient tissues. RESULTS: We demonstrate that invasion of metastatic cancer cells into the adjacent brain is associated with local recurrence and shortened overall survival. scRNAseq of paired tumor and adjacent brain samples confirmed the existence of invasive cancer cells in the tumor-adjacent brain. Analysis of these cells identified cold-inducible RNA-binding protein (CIRBP) overexpression in invasive cancer cells compared to cancer cells located within the metastases. Applying PDX models that recapitulate the invasion pattern observed in patients, we show that CIRBP is overexpressed in highly invasive BrM and is required for efficient invasive growth in the brain. CONCLUSIONS: These data demonstrate peritumoral invasion as a driver of treatment failure in BrM that is functionally mediated by CIRBP. These findings improve our understanding of the biology underlying postoperative treatment failure and lay the groundwork for rational clinical trial development based upon invasion pattern in surgically resected BrM.