Multiple analyses suggests texture features can indicate the presence of tumor in the prostate tissue.

Several studies have demonstrated statistical and texture analysis abilities to differentiate cancerous from healthy tissue in magnetic resonance imaging. This study developed a method based on texture analysis and machine learning to differentiate prostate findings. Forty-eight male patients with PI-RADS classification and subsequent radical prostatectomy histopathological analysis were used as gold standard. Experienced radiologists delimited the regions of interest in magnetic resonance images. Six different groups of images were used to perform multiple analyses (seven analyses variations). Those analyses were outlined by specialists in urology as those of most significant importance for the classification. Forty texture features were extracted from each image and processed with Random Forest, Support Vector Machine, K-Nearest Neighbors, and Naive Bayes. Those seven analyses variation results were described in terms of area under the ROC curve (AUC), accuracy, F-score, precision and sensitivity. The highest AUC (93.7%) and accuracy (88.8%) were obtained when differentiating the group with both MRI and histopathology positive findings against the group with both negative MRI and histopathology. When differentiating the group with both MRI and histopathology positive findings versus the peripheral image zone group the AUC value was 86.6%. When differentiating the group with negative MRI/positive histopathology versus the group with both negative MRI and histopathology the AUC value was 80.7%. The evaluation of statistical and texture analysis promoted very suggestive indications for future work in prostate cancer suspicious regions. The method is fast for both region of interest selection and classification with machine learning and the result brings original contributions in the classification of different groups of patients. This tool is low-cost, and can be used to assist diagnostic decisions.

Evaluation of exposure of a multidisciplinary team to ionizing radiation due to the use of fluoroscopy equipment in a surgical center.

INTRODUCTION: Ionizing radiation-producing equipment is used in surgical centers to guide invasive procedures. Technological advances have enabled improvements in image quality, which may be accompanied by increased radiation doses in the surgical team. Correct use of personal protective equipment and monitoring of radiation levels are required to a safe practice. OBJECTIVES: To evaluate radiation exposure conditions in occupationally exposed persons working at the Surgical Center at Hospital das Clínicas da Faculdade de Medicina de Botucatu for implementation of radiation protection measures. METHODS: Three different types of fluoroscopy equipment were used: C-arms, a dosimetric system with ionization chambers, and optically stimulated dosimeters. A three-stage evaluation was conducted, consisting of a first stage for observation, a second stage for estimation of kerma rate simulating exposure conditions, and a final stage for dosimetry to estimate the effective dose in workers. RESULTS: The most frequent procedures and the disposition for each team member were determined. Kerma values were estimated for both the principal physician and the assistant physician. The maximum number of annual procedures was also estimated so that the dose limits are not exceeded. CONCLUSIONS: Dosimetry for the surgical team is indicated as an approach to monitor occupational dose levels. The dose rates and effective dose found in this study are low but not negligible. Thus, proper use of equipment and periodic training for workers are still the best options for radiation protection.

Automatic algorithm for quantifying lung involvement in patients with chronic obstructive pulmonary disease, infection with SARS-CoV-2, paracoccidioidomycosis and no lung disease patients.

In this work, we aimed to develop an automatic algorithm for the quantification of total volume and lung impairments in four different diseases. The quantification was completely automatic based upon high resolution computed tomography exams. The algorithm was capable of measuring volume and differentiating pulmonary involvement including inflammatory process and fibrosis, emphysema, and ground-glass opacities. The algorithm classifies the percentage of each pulmonary involvement when compared to the entire lung volume. Our algorithm was applied to four different patients groups: no lung disease patients, patients diagnosed with SARS-CoV-2, patients with chronic obstructive pulmonary disease, and patients with paracoccidioidomycosis. The quantification results were compared with a semi-automatic algorithm previously validated. Results confirmed that the automatic approach has a good agreement with the semi-automatic. Bland-Altman (B&A) demonstrated a low dispersion when comparing total lung volume, and also when comparing each lung impairment individually. Linear regression adjustment achieved an R value of 0.81 when comparing total lung volume between both methods. Our approach provides a reliable quantification process for physicians, thus impairments measurements contributes to support prognostic decisions in important lung diseases including the infection of SARS-CoV-2.

Texture analysis to differentiate anterior cruciate ligament in patients after surgery with platelet-rich plasma.

BACKGROUND: Platelet-rich plasma (PRP) has been used to favor anterior cruciate ligament (ACL) healing after reconstruction surgeries. However, clinical data are still inconclusive and subjective about PRP. Thus, we propose a quantitative method to demonstrate that PRP produced morphological structure changes. METHODS: Thirty-four patients undergoing ACL reconstruction surgery were evaluated and divided into control group (sixteen patients) without PRP application and experiment group (eighteen patients) with intraoperative application of PRP. Magnetic resonance imaging (MRI) scans were performed 3 months after surgery. We used Matlab® and machine learning (ML) in Orange Canvas® to texture analysis (TA) features extraction. Experienced radiologists delimited the regions of interest (RoIs) in the T2-weighted images. Sixty-two texture parameters were extracted, including gray-level co-occurrence matrix and gray level run length. We used the algorithms logistic regression (LR), naive Bayes (NB), and stochastic gradient descent (SGD). RESULTS: The accuracy of the classification with NB, LR, and SGD was 83.3%, 75%, 75%, respectively. For the area under the curve, NB, LR, and SGD presented values of 91.7%, 94.4%, 75%, respectively. In clinical evaluations, the groups show similar responses in terms of improvement in pain and increase in the IKDC index (International Knee Documentation Committee) and Lysholm score indices differing only in the assessment of flexion, which presents a significant difference for the group treated with PRP. CONCLUSIONS: Here, we demonstrated quantitatively that patients who received PRP presented texture changes when compared to the control group. Thus, our findings suggest that PRP interferes with morphological parameters of the ACL. TRIAL REGISTRATION: Protocol no. CAAE 56164316.6.0000.5411.

Inflammatory lesions and brain tumors: is it possible to differentiate them based on texture features in magnetic resonance imaging?

BACKGROUND: Neuroimaging strategies are essential to locate, to elucidate the etiology, and to the follow up of brain disease patients. Magnetic resonance imaging (MRI) provides good cerebral soft-tissue contrast detection and diagnostic sensitivity. Inflammatory lesions and tumors are common brain diseases that may present a similar pattern of a cerebral ring enhancing lesion on MRI, and non-enhancing core (which may reflect cystic components or necrosis) leading to misdiagnosis. Texture analysis (TA) and machine learning approaches are computer-aided diagnostic tools that can be used to assist radiologists in such decisions. METHODS: In this study, we combined texture features with machine learning (ML) methods aiming to differentiate brain tumors from inflammatory lesions in magnetic resonance imaging. Retrospective examination of 67 patients, with a pattern of a cerebral ring enhancing lesion, 30 with inflammatory, and 37 with tumoral lesions were selected. Three different MRI sequences and textural features were extracted using gray level co-occurrence matrix and gray level run length. All diagnoses were confirmed by histopathology, laboratorial analysis or MRI. RESULTS: The features extracted were processed for the application of ML methods that performed the classification. T1-weighted images proved to be the best sequence for classification, in which the differentiation between inflammatory and tumoral lesions presented high accuracy (0.827), area under ROC curve (0.906), precision (0.837), and recall (0.912). CONCLUSION: The algorithm obtained textures capable of differentiating brain tumors from inflammatory lesions, on T1-weghted images without contrast medium using the Random Forest machine learning classifier.

Inflammatory lesions and brain tumors: is it possible to differentiate them based on texture features in magnetic resonance imaging?

Neuroimaging strategies are essential to locate, to elucidate the etiology, and to the follow up of brain disease patients. Magnetic resonance imaging (MRI) provides good cerebral soft-tissue contrast detection and diagnostic sensitivity. Inflammatory lesions and tumors are common brain diseases that may present a similar pattern of a cerebral ring enhancing lesion on MRI, and non-enhancing core (which may reflect cystic components or necrosis) leading to misdiagnosis. Texture analysis (TA) and machine learning approaches are computer-aided diagnostic tools that can be used to assist radiologists in such decisions. Methods: In this study, we combined texture features with machine learning (ML) methods aiming to differentiate brain tumors from inflammatory lesions in magnetic resonance imaging. Retrospective examination of 67 patients, with a pattern of a cerebral ring enhancing lesion, 30 with inflammatory, and 37 with tumoral lesions were selected. Three different MRI sequences and textural features were extracted using gray level co-occurrence matrix and gray level run length. All diagnoses were confirmed by histopathology, laboratorial analysis or MRI. Results: The features extracted were processed for the application of ML methods that performed the classification. T1-weighted images proved to be the best sequence for classification, in which the differentiation between inflammatory and tumoral lesions presented high accuracy (0.827), area under ROC curve (0.906), precision (0.837), and recall (0.912). Conclusion: The algorithm obtained textures capable of differentiating brain tumors from inflammatory lesions, on T1-weghted images without contrast medium using the Random Forest machine learning classifier.(AU)

Radiographic predictors determined with an objective assessment tool for neonatal patients with necrotizing enterocolitis / Preditores radiográficos determinados com uso de ferramenta de avaliação objetiva para pacientes neonatais com enterocolite necrotizante

ABSTRACT Objective: The objective of this study was to develop and validate a computational tool to assist radiological decisions on necrotizing enterocolitis. Methodology: Patients that exhibited clinical signs and radiographic evidence of Bell's stage 2 or higher were included in the study, resulting in 64 exams. The tool was used to classify localized bowel wall thickening and intestinal pneumatosis using full-width at half-maximum measurements and texture analyses based on wavelet energy decomposition. Radiological findings of suspicious bowel wall thickening and intestinal pneumatosis loops were confirmed by both patient surgery and histopathological analysis. Two experienced radiologists selected an involved bowel and a normal bowel in the same radiography. The full-width at half-maximum and wavelet-based texture feature were then calculated and compared using the Mann-Whitney U test. Specificity, sensibility, positive and negative predictive values were calculated. Results: The full-width at half-maximum results were significantly different between normal and distended loops (median of 10.30 and 15.13, respectively). Horizontal, vertical, and diagonal wavelet energy measurements were evaluated at eight levels of decomposition. Levels 7 and 8 in the horizontal direction presented significant differences. For level 7, median was 0.034 and 0.088 for normal and intestinal pneumatosis groups, respectively, and for level 8 median was 0.19 and 0.34, respectively. Conclusions: The developed tool could detect differences in radiographic findings of bowel wall thickening and IP that are difficult to diagnose, demonstrating the its potential in clinical routine. The tool that was developed in the present study may help physicians to investigate suspicious bowel loops, thereby considerably improving diagnosis and clinical decisions.

RESUMO Objetivo: O objetivo deste estudo foi desenvolver e validar uma ferramenta computacional para auxiliar as decisões radiológicas na enterocolite necrotizante. Metodologia: Pacientes que exibiam sinais clínicos e evidências radiográficas do estágio 2 ou superior de Bell foram incluídos no estudo, que resultou em 64 exames. A ferramenta foi usada para classificar o aumento localizado da espessura da parede intestinal e a pneumatose intestinal com medidas de largura total a meia altura e análises de textura baseadas na decomposição da energia wavelet. Os achados radiológicos de aumento suspeito da espessura da parede intestinal e das alças na pneumatose intestinal foram confirmados pela cirurgia e análise histopatológica do paciente. Dois radiologistas experientes selecionaram um intestino afetado e um intestino normal na mesma radiografia. A largura total a meia altura e a característica da textura baseada em wavelet foram então calculadas e comparadas com o uso do teste U de Mann-Whitney. Foram calculados a especificidade, sensibilidade, valores preditivos positivos e negativos. Resultados: Os resultados da largura total a meia altura foram significativamente diferentes entre a alça normal e a distendida (mediana de 10,30 e 15,13, respectivamente). Medidas de energia wavelet horizontal, vertical e diagonal foram avaliadas em oito níveis de decomposição. Os níveis 7 e 8 na direção horizontal apresentaram diferenças significativas. Para o nível 7, as medianas foram 0,034 e 0,088 para os grupos normal e com pneumatose intestinal, respectivamente, e para o nível 8, as medianas foram 0,19 e 0,34, respectivamente. Conclusões: A ferramenta desenvolvida pode detectar diferenças nos achados radiográficos do aumento da espessura da parede intestinal e PI de difícil diagnóstico, demonstra seu potencial na rotina clínica. A ferramenta desenvolvida no presente estudo pode ajudar os médicos a investigar alças intestinais suspeitas e melhorar consideravelmente o diagnóstico e as decisões clínicas.

Cochlear implants: Insertion assessment by computed tomography.

BACKGROUND AND OBJECTIVES: Imaging exams play a key role in cochlear implants with regard to both planning implantation before surgery and quality control after surgery. The ability to visualize the three-dimensional location of implanted electrodes is useful in clinical routines for assessing patient outcome. The aim of this study was to evaluate linear and angular insertion depth measurements of cochlear implants based on conventional computed tomography. METHODS: Tools for linear and angular measurements of cochlear implants were used in computed tomography exams. The tools realized the insertion measurements in an image reconstruction of the CIs, based on image processing techniques. We comprehensively characterized two cochlear implant models while obviating possible changes that can be caused by different cochlea sizes by using the same human temporal bones to evaluate the implant models. RESULTS: The tools used herein were able to differentiate the insertion measurements between two cochlear implant models widely used in clinical practice. We observed significant differences between both insertion measurements because of their different design and construction characteristics (p = 0.004 and 0.003 for linear and angular measurements, respectively; t-test). The presented methodology showed to be a good tool to calculate insertion depth measurements, since it is easy to perform, produces high-resolution images, and is able to depict all the landmarks, thus enabling measurement of the angular and linear insertion depth of the most apical electrode contacts. CONCLUSION: The present study demonstrates practical and useful tools for evaluating cochlear implant electrodes in clinical practice. Further studies should measure preoperative and postoperative benefits in terms of speech recognition and evaluate the preservation of residual hearing in the implanted ear. Such studies can also determine correlations between surgical factors, electrode positions, and performance. In addition to refined surgical techniques, the precise evaluation of cochlear length and correct choice of cochlear implant characteristics can play an important role in postoperative outcomes.

Ischemic stroke enhancement using a variational model and the expectation maximization method.

OBJECTIVES: In order to enable less experienced physicians to reliably detect early signs of stroke, A novel approach was proposed to enhance the visual perception of ischemic stroke in non-enhanced CT. METHODS: A set of 39 retrospective CT scans were used, divided into 23 cases of acute ischemic stroke and 16 normal patients. Stroke cases were obtained within 4.5 h of symptom onset and with a mean NIHSS of 12.9±7.4. After selection of adjunct slices from the CT exam, image averaging was performed to reduce the noise and redundant information. This was followed by a variational decomposition model to keep the relevant component of the image. The expectation maximization method was applied to generate enhanced images. RESULTS: We determined a test to evaluate the performance of observers in a clinical environment with and without the aid of enhanced images. The overall sensitivity of the observer's analysis was 64.5 % and increased to 89.6 % and specificity was 83.3 % and increased to 91.7 %. CONCLUSION: These results show the importance of a computational tool to assist neuroradiology decisions, especially in critical situations such as the diagnosis of ischemic stroke. KEY POINTS: • Diagnosing patients with stroke requires high efficiency to avoid irreversible cerebral damage. • A computational algorithm was proposed to enhance the visual perception of stroke. • Observers' performance was increased with the aid of enhanced images.

Efficiency of personal dosimetry methods in vascular interventional radiology.

PURPOSE: The aim of the present study was to determine the efficiency of six methods for calculate the effective dose (E) that is received by health professionals during vascular interventional procedures. METHODS: We evaluated the efficiency of six methods that are currently used to estimate professionals' E, based on national and international recommendations for interventional radiology. Equivalent doses on the head, neck, chest, abdomen, feet, and hands of seven professionals were monitored during 50 vascular interventional radiology procedures. Professionals' E was calculated for each procedure according to six methods that are commonly employed internationally. To determine the best method, a more efficient E calculation method was used to determine the reference value (reference E) for comparison. RESULTS: The highest equivalent dose were found for the hands (0.34±0.93mSv). The two methods that are described by Brazilian regulations overestimated E by approximately 100% and 200%. The more efficient method was the one that is recommended by the United States National Council on Radiological Protection and Measurements (NCRP). The mean and median differences of this method relative to reference E were close to 0%, and its standard deviation was the lowest among the six methods. CONCLUSIONS: The present study showed that the most precise method was the one that is recommended by the NCRP, which uses two dosimeters (one over and one under protective aprons). The use of methods that employ at least two dosimeters are more efficient and provide better information regarding estimates of E and doses for shielded and unshielded regions.

Occupational radiation exposure in vascular interventional radiology: A complete evaluation of different body regions.

PURPOSE: To perform a complete evaluation on radiation doses, received by primary and assistant medical staff, while performing different vascular interventional radiology procedures. MATERIALS AND METHODS: We evaluated dose received in different body regions during three categories of vascular procedures: lower limb angiography (Angiography), lower limb percutaneous transluminal angioplasty (Angioplasty) and stent graft placement for abdominal aortic aneurysm treatment (A. A. A. Treatment). We positioned the dosimeters near the eye lens, thyroid, chest, abdomen, hands, and feet of the interventional physicians. Equivalent dose was compared with annual dose limits for workers in order to determine the maximum number of procedures per year that each physician could perform. We assessed 90 procedures. RESULTS: We found the highest equivalent doses in the A. A. A. Treatment, in which 90% of the evaluations indicated at least one region receiving more than 1mSv per procedure. Angioplasty was the only procedural modality that provided statistically different doses for different professionals, which is an important aspect on regards to radiological protection strategies. In comparison with the dose limits, the most critical region in all procedures was the eye lens. CONCLUSIONS: Since each body region of the interventionist is exposed to different radiation levels, dose distribution measurements are essential for radiological protection strategies. These results indicate that dosimeters placed in abdomen instead of chest may represent more accurately the whole body doses received by the medical staff. Additional dosimeters and a stationary shield for the eye lens are strongly recommended.

Construction of pediatric homogeneous phantoms for optimization of chest and skull radiographs.

OBJECTIVES: To develop two pediatric patient-equivalent phantoms, the Pediatric Chest Equivalent Patient (PCEP) and the Pediatric Skull Equivalent Patient (PSEP) for children aged 1 to 5 years. We also used both phantoms for image quality evaluations in computed radiography systems to determine Gold Standard (GS) techniques for pediatric patients. METHODS: To determine the simulator materials thickness (Lucite and aluminum), we quantified biological tissues (lung, soft, and bone) using an automatic computational algorithm. To objectively establish image quality levels, two physical quantities were used: effective detective quantum efficiency and contrast-to-noise ratio. These quantities were associated to values obtained for standard patients from previous studies. RESULTS: For chest radiographies, the GS technique applied was 81kVp, associated to 2.0mAs and 83.6µGy of entrance skin dose (ESD), while for skull radiographies, the GS technique was 70kVp, associated to 5mAs and 339µGy of ESD. CONCLUSION: This procedure allowed us to choose optimized techniques for pediatric protocols, thus improving quality of diagnosis for pediatric population and reducing diagnostic costs to our institution. These results could also be easily applied to other services with different equipment technologies.