Tolerability and toxicity of prophylactic cranial irradiation in patients with non-small cell lung cancer - Results of a phase II study (with estimation of hematological toxicity, pituitary function and magnetic resonance spectra changes).

AIM: To evaluate the tolerability and toxicity of PCI in patients with NSCLC. BACKGROUND: Prophylactic cranial irradiation (PCI) is a standard treatment for patients with small cell lung cancer. There are data showing a decreasing ratio of brain metastases after PCI for non-small cell lung cancer (NSCLC-non small cell lung cancer) patients but, so far, there is no evidence for increasing overall survival. The main concern in this setting is the tolerance and toxicity of the treatment. MATERIALS AND METHODS: From 1999 to 2007, 50 patients with NSCLC treated with radical intent underwent PCI (30 Gy in 15 fractions). Mean follow-up was 2.8 years. The tolerability and hematological toxicity were evaluated in all patients, a part of participants had done neuropsychological tests, magnetic resonance imaging with (1)H nuclear magnetic resonance spectra, and estimation of pituitary function. RESULTS: During follow-up, 20 patients developed distant metastases, 4-brain metastases. Fourteen (30%) patients had acute side effects: (headache, nausea, erythema of the skin). The symptoms did not require treatment breaks. Six patients complained of late side effects (vertigo, nausea, anxiety, lower extremity weakness, deterioration of hearing and olfactory hyperesthesia). Hematological complications were not observed. Testosterone levels tended to decrease (p = 0.062). Visual-motor function deteriorated after treatment (p < 0.059). Performance IQ decreased (p < 0.025) and the difference between performance IQ and verbal IQ increased (p < 0.011). Degenerative periventricular vascular changes were observed in two patients. Analysis of the spectroscopic data showed metabolic but reversible alterations after PCI. CONCLUSION: PCI in the current series was well tolerated and associated with a relatively low toxicity.

Deep learning ensembles for detecting brain metastases in longitudinal multi-modal MRI studies.

Metastatic brain cancer is a condition characterized by the migration of cancer cells to the brain from extracranial sites. Notably, metastatic brain tumors surpass primary brain tumors in prevalence by a significant factor, they exhibit an aggressive growth potential and have the capacity to spread across diverse cerebral locations simultaneously. Magnetic resonance imaging (MRI) scans of individuals afflicted with metastatic brain tumors unveil a wide spectrum of characteristics. These lesions vary in size and quantity, spanning from tiny nodules to substantial masses captured within MRI. Patients may present with a limited number of lesions or an extensive burden of hundreds of them. Moreover, longitudinal studies may depict surgical resection cavities, as well as areas of necrosis or edema. Thus, the manual analysis of such MRI scans is difficult, user-dependent and cost-inefficient, and - importantly - it lacks reproducibility. We address these challenges and propose a pipeline for detecting and analyzing brain metastases in longitudinal studies, which benefits from an ensemble of various deep learning architectures originally designed for different downstream tasks (detection and segmentation). The experiments, performed over 275 multi-modal MRI scans of 87 patients acquired in 53 sites, coupled with rigorously validated manual annotations, revealed that our pipeline, built upon open-source tools to ensure its reproducibility, offers high-quality detection, and allows for precisely tracking the disease progression. To objectively quantify the generalizability of models, we introduce a new data stratification approach that accommodates the heterogeneity of the dataset and is used to elaborate training-test splits in a data-robust manner, alongside a new set of quality metrics to objectively assess algorithms. Our system provides a fully automatic and quantitative approach that may support physicians in a laborious process of disease progression tracking and evaluation of treatment efficacy.

Detecting liver cirrhosis in computed tomography scans using clinically-inspired and radiomic features.

Hepatic cirrhosis is an increasing cause of mortality in developed countries-it is the pathological sequela of chronic liver diseases, and the final liver fibrosis stage. Since cirrhosis evolves from the asymptomatic phase, it is of paramount importance to detect it as quickly as possible, because entering the symptomatic phase commonly leads to hospitalization and can be fatal. Understanding the state of the liver based on the abdominal computed tomography (CT) scans is tedious, user-dependent and lacks reproducibility. We tackle these issues and propose an end-to-end and reproducible approach for detecting cirrhosis from CT. It benefits from the introduced clinically-inspired features that reflect the patient's characteristics which are often investigated by experienced radiologists during the screening process. Such features are coupled with the radiomic ones extracted from the liver, and from the suggested region of interest which captures the liver's boundary. The rigorous experiments, performed over two heterogeneous clinical datasets (two cohorts of 241 and 32 patients) revealed that extracting radiomic features from the liver's rectified contour is pivotal to enhance the classification abilities of the supervised learners. Also, capturing clinically-inspired image features significantly improved the performance of such models, and the proposed features were consistently selected as the important ones. Finally, we showed that selecting the most discriminative features leads to the Pareto-optimal models with enhanced feature-level interpretability, as the number of features was dramatically reduced (280×) from thousands to tens.

The Effectiveness and Toxicity of Frameless CyberKnife Based Radiosurgery for Parkinson's Disease-Phase II Study.

Frame-based stereotactic radiosurgery (SRS) has an established role in the treatment of tremor in patients with Parkinson's disease (PD). The low numbers of studies of frameless approaches led to our prospective phase 2 open-label single-arm clinical trial (NCT02406105), which aimed to evaluate the safety and efficacy of CyberKnife frameless SRS. Twenty-three PD patients were irradiated on the area of the thalamic ventral nuclei complex with gradually increasing doses of 70 to 105 Gy delivered in a single fraction. After SRS, patients were monitored for tremor severity and the toxicity of the treatment. Both subjective improvement and dose-dependent efficacy were analysed using standard statistical tests. The median follow-up was 23 months, and one patient died after COVID-19 infection. Another two patients were lost from follow-up. Hyper-response resulting in vascular toxicity and neurologic complications was observed in two patients irradiated with doses of 95 and 100 Gy, respectively. A reduction in tremor severity was observed in fifteen patients, and six experienced stagnation. A constant response during the whole follow-up was observed in 67% patients. A longer median response time was achieved in patients irradiated with doses equal to or less than 85 Gy. Only two patients declared no improvement after SRS. The efficacy of frameless SRS is high and could improve tremor control in a majority of patients. The complication rate is low, especially when doses below 90 Gy are applied. Frameless SRS could be offered as an alternative for patients ineligible for deep brain stimulation; however, studies regarding optimal dose are required.

Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients.

Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma. This assessment is, however, complex to perform and associated with high variability due to the high heterogeneity and complexity of the disease. In this work, we tackle this issue and propose a deep learning pipeline for the fully automated end-to-end analysis of glioblastoma patients. Our approach simultaneously identifies tumor sub-regions, including the enhancing tumor, peritumoral edema and surgical cavity in the first step, and then calculates the volumetric and bidimensional measurements that follow the current Response Assessment in Neuro-Oncology (RANO) criteria. Also, we introduce a rigorous manual annotation process which was followed to delineate the tumor sub-regions by the human experts, and to capture their segmentation confidences that are later used while training deep learning models. The results of our extensive experimental study performed over 760 pre-operative and 504 post-operative adult patients with glioma obtained from the public database (acquired at 19 sites in years 2021-2020) and from a clinical treatment trial (47 and 69 sites for pre-/post-operative patients, 2009-2011) and backed up with thorough quantitative, qualitative and statistical analysis revealed that our pipeline performs accurate segmentation of pre- and post-operative MRIs in a fraction of the manual delineation time (up to 20 times faster than humans). Volumetric measurements were in strong agreement with experts with the Intraclass Correlation Coefficient (ICC): 0.959, 0.703, 0.960 for ET, ED, and cavity. Similarly, automated RANO compared favorably with experienced readers (ICC: 0.681 and 0.866) producing consistent and accurate results. Additionally, we showed that RANO measurements are not always sufficient to quantify tumor burden. The high performance of the automated tumor burden measurement highlights the potential of the tool for considerably improving and simplifying radiological evaluation of glioblastoma in clinical trials and clinical practice.

Non-parametric MRI Brain Atlas for the Polish Population.

Introduction: The application of magnetic resonance imaging (MRI) to acquire detailed descriptions of the brain morphology in vivo is a driving force in brain mapping research. Most atlases are based on parametric statistics, however, the empirical results indicate that the population brain tissue distributions do not exhibit exactly a Gaussian shape. Our aim was to verify the population voxel-wise distribution of three main tissue classes: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), and to construct the brain templates for the Polish (Upper Silesian) healthy population with the associated non-parametric tissue probability maps (TPMs) taking into account the sex and age influence. Material and Methods: The voxel-wise distributions of these tissues were analyzed using the Shapiro-Wilk test. The non-parametric atlases were generated from 96 brains of the ethnically homogeneous, neurologically healthy, and radiologically verified group examined in a 3-Tesla MRI system. The standard parametric tissue proportion maps were also calculated for the sake of comparison. The maps were compared using the Wilcoxon signed-rank test and Kolmogorov-Smirnov test. The volumetric results segmented with the parametric and non-parametric templates were also analyzed. Results: The results confirmed that in each brain structure (regardless of the studied sub-population) the data distribution is skewed and apparently not Gaussian. The determined non-parametric and parametric templates were statistically compared, and significant differences were found between the maps obtained using both measures (the maps of GM, WM, and CSF). The impacts of applying the parametric and non-parametric TPMs on the segmentation process were also compared. The GM volumes are significantly greater when using the non-parametric atlas in the segmentation procedure, while the CSF volumes are smaller. Discussion and Conclusion: To determine the population atlases the parametric measures are uncritically and widely used. However, our findings suggest that the mean and parametric measures of such skewed distribution may not be the most appropriate summary statistic to find the best spatial representations of the structures in a standard space. The non-parametric methodology is more relevant and universal than the parametric approach in constructing the MRI brain atlases.

Neuropsychological assessment of patients undergoing surgery due to low-grade glioma involving the supplementary motor area.

OBJECTIVE: The aim of the study was to establish the usefulness of various neuropsychological tests in patients undergoing surgery due to low-grade glioma (LGG) involving supplementary motor area (SMA). PATIENTS AND METHODS: 21 patients diagnosed with LGG involving the SMA underwent, before and after surgery, screening tests (Mini Mental State Examination - MMSE, Montreal Cognitive Assessment - MoCA and Frontal Assessment Battery - FAB), tests to assess language functions (Addenbrook's Cognitive Examination-III - ACE-III, phonetic fluency tests and semantic fluency tests), tests to assess memory functions (Rey's 15-word test - RAVLT and Diagnostic Test for Brain Damage by Hillers- DCS), tests to assess executive functions (Wisconsin Card Sorting Test - WCST, Ruff Figural Fluency Test - RFFT and Trail Making Test). RESULTS: Before surgery, in the screening tests the patients obtained below normal scores in the MoCA test only. After surgery, the scores of such tests were significantly worse than the scores before surgery, while the MMSE test scores continued to be within the normal range. In phonetic and semantic fluency tests, the patients obtained worse scores, both before and after surgery. The differences in the test scores between the two periods, i.e. before and after surgery, were statistically significant. Although the patients obtained worse scores both before and after surgery in the ACE III test, the differences in the test scores were not statistically significant. The scores obtained in all tests assessing the executive and memory functions before and after surgery were within the normal range. The scores in such tests (except the WCCT) dropped significantly after surgery. CONCLUSIONS: Patients diagnosed with LGG involving the SMA reveal the impairment of cognitive functions, in particular language functions. After surgery, a significant impairment of all elementary cognitive functions, such as attention, memory, language and executive functions and complex cognitive functions, occurs. The most sensitive tests to detect cognitive disorders, executive dysfunctions and speech disturbances in patients undergoing surgery due to glioma include the MoCA, FAB and Phonetic and Semantic Fluency Tests.

Radioactive iodine (RAI) treatment of hyperthyroidism is safe in patients with Graves' orbitopathy--a prospective study.

INTRODUCTION: Radioactive iodine (RAI) therapy may induce or worsen orbitopathy (GO) in Graves' disease (GD). The aim of this study was a prospective assessment of the risk of GO exacerbation in a GD patients cohort submitted to RAI therapy for hyperthyroidism. MATERIAL AND METHODS: 208 consecutive GD patients treated with 131I in 2007 were enrolled. The analysis was performed on 156 patients strictly monitored for one year. Glucocorticosteroid (GCS) prophylaxis was administered if GO symptoms or GO history were present, and in cases of tobacco smokers even without GO symptoms. Clinical and biochemical evaluation at one, three, six, and 12 months after therapy was performed in the whole group, then at 24 months in 138 patients. RESULTS: There was no severe GO progression in patients without GO symptoms at the time of RAI treatment. The risk of severe GO worsening for preexisting GO patients (demanding systemic GCS administration) during the 12-month follow-up after RAI therapy was 10%. 12 and 24 months after 131I administration, stable improvement compared to the initial GO status had been achieved in most (98-96%) patients. CONCLUSIONS: 1. In patients with mild GO, the risk of severe GO worsening after RAI therapy is acceptable, as long as RAI therapy is applied with GCS cover. 2. In patients without GO symptoms at the time of RAI therapy but with a history of GO and with subclinical GO diagnosed by MRI only, the risk of severe progression is minimal. 3. Distant outcomes of RAI treatment confirmed its safety in GO patients.

Differentiation between brain tumor recurrence and radiation injury using perfusion, diffusion-weighted imaging and MR spectroscopy.

BACKGROUND: Differentiation between tumor recurrence/vital tumor tissue and radionecrosis based on conventional diagnostic imaging is impossible because of the likeness of the images. In such circumstances advanced MRI techniques (PWI, DWI, 1HMRS) seem to be helpful. The aim of our study was to evaluate the diagnostic effectiveness of PWI, DWI and 1HMRS in the differentiation of the tumor recurrence from radiation related injury. MATERIAL AND METHODS: The retrospective analysis comprised 11 contrast-enhancing lesions observed in 8 patients treated for gliomas with radiotherapy or radiochemotherapy. 5 out of 11 contrast-enhancing lesions were tumor recurrences whereas 6 out of 11 radiation-related injuries. The MR examinations comprised of conventional MR imaging (T1-SE, T1-MPRAGE with CE, T2-TSE, T2 FLAIR) and PWI, DWI, 1HMRS. Mean and maximum rCBV values of each contrast-enhancing lesion were calculated. These values were normalized to normal appearing white matter. Mean normalized ADC ratio to normal appearing white matter and mean ADC obtained from contrast-enhancing lesions were analysed. In 1HMRS only those voxels which were placed in solid part of the contrast-enhancing lesion were analysed and Cho/Cr, Cho/NAA ratios presented. RESULTS: Mean normalized rCBVmax (2.44 +/- 0.73 for tumor recurrence vs. 0.78 +/- 0.46 for radiation injury; p < 0.001) and mean normalized rCBVmean (1.46 +/- 0.49 for tumor recurrence vs. 0.49 +/- 0.38 for radiation injury; p < 0.005) were significantly higher in the recurrent gliomas group than in the radiation injury one. It was observed that normalized rCBVmax higher than 1.7 and normalized rCBVmean higher than 1.25 is highly indicative for recurrent glioma whereas normalized rCBVmax lower than 1.0 and normalized rCBVmean lower than 0.5 is highly indicative for radiation injury. Results obtained in DWI and 1HMRS were not statistically significant different between two analysed groups. Mean ADCce: 1.06 +/- 0.18 x 10-3 mm2/s for tumor recurrence vs. 1.13 +/- 0.13 x 10-3 mm2/s for radiation injury; p = 0.51. Mean normalized ADC: 1.55 +/- 0.39 x 10-3 mm2/s for tumor recurrence vs. 1.55 +/- 0.18 x 10-3 mm2/s for radiation injury; p = 0.98. Median Cho/Cr ratio: (2.16min/max [1.67-3.15] for tumor recurrence vs. 1.34min/max [1.13-2.37] for radiation injury; p = 0.15), median Cho/NAA ratio (1.9min/max [0.86-2.36] for tumor recurrence vs. 2.11min/max [0.97 vs. 2.87] for radiation injury; p = 0.51). CONCLUSIONS: Among the analyzed advanced neuroimaging methods PWI seems to be most reliable in differentiation between tumor regrowth/recurrence and radiation necrosis. In these results mean rCBV is a better differing factor than max rCBV. Proton MR spectroscopy (1HMRS) and DWI do not differentiate analyzed groups with statistical significance, despite tendency to lower ADC values in recurrence group than in radiation injury one.