Primary pleural lymphoma of T cell origin in a paediatric patient with a focus on radiological findings : a case report and review of literature

Purpose: Pleural lymphoma is a medical condition characterised by shortness of breath and obscure chest pain, which may be a diagnostic challenge, especially when it occurs in children. Plain chest X-rays and computed tomography (CT) scan are the main imaging techniques and are the initial diagnostic methods utilised. Case report: A four-year-old boy was admitted to the emergency ward with pain in the right thoracoabdominal region, which had persisted for two months. Physical examination revealed reduced respiratory sounds in the right chest, but with no other significant findings. The patient underwent chest X-ray and CT scan, which showed right sided pleural thickening coupled with a massive pleural effusion. The patient underwent pleural biopsy, and a diagnosis of T-cell primary pleural lymphoma was made. The patient underwent treatment with BFM-NHL and was followed for three years, during which the patient remained disease free. Conclusions: Pleural lymphoma is a rare diagnosis especially in children and could have overlapping clinical manifestations with more common conditions. Imaging techniques are the main route of clinical work-up towards final diagnosis. Here, we present a rare paediatric patient with no significant past medical history, who underwent imaging and was diagnosed with T-cell primary pleural lymphoma, a very rare subtype of primary pleural lymphoma

Prevalence of positive findings of brain computed tomography scans in pediatric population

  Objectives Computed tomography (CT) scans are used more frequently in medical centers, increasing unnecessary requests for it as a first-line evaluation. This study aimed to investigate the rate and prevalence of abnormal findings in a brain CT scan in children at the Children’s Hospital of Tabriz, Iran. Materials &Methods This study was a cross-sectional descriptive-analytical study that included all children under 15 years old undergoing a brain CT scan at the Children’s Hospital of Tabriz, Iran. All patients who fulfilled the inclusion criteria and were referred to the Children’s Hospital of Tabriz during the spring of 2018 entered the study. Age, gender, patient history, and clinical findings were examined. In the next step, the results of each patient’s CT scan were evaluated. Results In this study, 108 patients were studied with a median age of 18.0 months. CT scan results were normal in seventy-four cases (68.5%), hydrocephalus was seen in 15 (13.9%), and benign infantile hydrocephalus was seen in eight (7.4%). The study revealed a statistically significant relationship between patient history and CT scan results (p=0.017). A statistically significant relationship was observed between the clinical findings and CT scan results (p=0.042). Conclusion Brain CT scans have more abnormal findings in patients with positive clinical findings. Although a CT scan is a highly sensitive and specifimodality in diagnosing central nervous system (CNS) abnormalities, its value depends on the underlying medical history and physical exam

A new fuzzy reinforcement learning method for effective chemotherapy

A key challenge for drug dosing schedules is the ability to learn an optimal control policy even when there is a paucity of accurate information about the systems. Artificial intelligence has great potential for shaping a smart control policy for the dosage of drugs for any treatment. Motivated by this issue, in the present research paper a Caputo–Fabrizio fractional-order model of cancer chemotherapy treatment was elaborated and analyzed. A fix-point theorem and an iterative method were implemented to prove the existence and uniqueness of the solutions of the proposed model. Afterward, in order to control cancer through chemotherapy treatment, a fuzzy-reinforcement learning-based control method that uses the State-Action-Reward-State-Action (SARSA) algorithm was proposed. Finally, so as to assess the performance of the proposed control method, the simulations were conducted for young and elderly patients and for ten simulated patients with different parameters. Then, the results of the proposed control method were compared with Watkins’s Q-learning control method for cancer chemotherapy drug dosing. The results of the simulations demonstrate the superiority of the proposed control method in terms of mean squared error, mean variance of the error, and the mean squared of the control action—in other words, in terms of the eradication of tumor cells, keeping normal cells, and the amount of usage of the drug during chemotherapy treatment

Radiomics reproducibility challenge in computed tomography imaging as a nuisance to clinical generalization: a mini-review

Abstract Background Radiomics has demonstrated striking potential in accurate cancer diagnosis but still needs strengthening of validity and standardization to achieve reproducible and generalizable results. Despite the advantages of radiomics, inter-scanner and intra-scanner variations of computed tomography (CT) scanning parameters can affect the reproducibility of its results. Accordingly, this article aims to review the impact of CT scanning parameters on the reproducibility of radiomics results. Main body of the abstract In general, radiomics results are sensitive to changes in the noise level; therefore, any parameter that affects image noise, such as kilovoltage (kVp), tube current (mAs), slice thickness, spatial resolution, image reconstruction algorithm, etc., can affect radiomics results. Also, region of interest (ROI) segmentation is another fundamental challenge in reducing radiomics reproducibility. Studies showed that almost all scanning parameters affect the reproducibility of radiomics. However, some robust features are reproducible. Short conclusion One of the solutions to overcome the radiomics reproducibility challenge is the standardization of imaging protocols according to noise level (not scanning protocols). The second solution is to list reproducible features according to the type of complication and anatomical region. Resampling may also overcome feature instability

Ultrasound assessment of diaphragm thickness in mechanically ventilated patients suffering from obstructive pulmonary disease and its relationship with outcomes of mechanical ventilation and mechanical parameters of lungs

Introduction: Successful weaning from the ventilator depends on several factors including muscular, cardiac, respiratory, and metabolic strength. Acquired weakness of diaphragm muscle caused by mechanical ventilation is one of the reasons for failure to wean patients from ventilators. Meanwhile, it has been shown that the thickening fraction (TF) and ultrasound of the diaphragm are proper non-invasive indicators for making decisions on weaning patients from the ventilator. Methodology: Mechanically ventilated patients with chronic obstructive pulmonary disease who had been admitted to pulmonary intensive care units during a period of one year were selected for this study. Through statistical consultation, sixty patients were examined for the study. There was no gender difference in the number of patients. After selecting the patients, all of them underwent a B-mode ultrasound with a linear probe of 9 to 12 megahertz on the second day after intubation in the supine position. The thickness of the diaphragm in all patients was measured at the end of exhalation and in the space between the 9th and 10th ribs, between the anterior and middle axillary lines, and the results were compared between the individuals in two ways: successful and unsuccessful weaning

A New Fuzzy Reinforcement Learning Method for Effective Chemotherapy

A key challenge for drug dosing schedules is the ability to learn an optimal control policy even when there is a paucity of accurate information about the systems. Artificial intelligence has great potential for shaping a smart control policy for the dosage of drugs for any treatment. Motivated by this issue, in the present research paper a Caputo–Fabrizio fractional-order model of cancer chemotherapy treatment was elaborated and analyzed. A fix-point theorem and an iterative method were implemented to prove the existence and uniqueness of the solutions of the proposed model. Afterward, in order to control cancer through chemotherapy treatment, a fuzzy-reinforcement learning-based control method that uses the State-Action-Reward-State-Action (SARSA) algorithm was proposed. Finally, so as to assess the performance of the proposed control method, the simulations were conducted for young and elderly patients and for ten simulated patients with different parameters. Then, the results of the proposed control method were compared with Watkins’s Q-learning control method for cancer chemotherapy drug dosing. The results of the simulations demonstrate the superiority of the proposed control method in terms of mean squared error, mean variance of the error, and the mean squared of the control action—in other words, in terms of the eradication of tumor cells, keeping normal cells, and the amount of usage of the drug during chemotherapy treatment

Findings of Brain CT Scan in Patients Hospitalized with Meningitis in Tabriz Children's Hospital and Its Association with Prognosis

Background. Children's meningitis is associated with many complications and deaths. Therefore, it is critical to develop reliable and available methods to predict these complications, diagnose them in a timely manner, and prevent the progression of complications. This study aimed to investigate the findings of computed tomography (CT) scan in patients with meningitis and evaluate its association with prognosis. Methods. In this cross-sectional analytical study, we investigated the clinical files of 100 patients with meningitis (preferably bacterial meningitis) in Tabriz Children's Hospital, Iran between 2013-2017. The results of CT scan and disease complications were recorded and analyzed by statistical software. Results. Common symptoms identified in the patients included fever (88%), nausea and vomiting (36%), headache (27%), and convulsions (25%). Also, the results of CT scan included cerebral effusion (16%), hydrocephalus (7%), diffuse cerebral edema (7%), and cerebral atrophy (4%). Statistical analysis indicated a statistically significant association between the complications of meningitis (learning disorder, epilepsy, and hearing disorder) and the results of CT scan (cerebral effusion, diffuse cerebral edema, and hydrocephalus). Conclusion. CT scan can be used as an early diagnostic and prognostic method in children with meningitis. Practical Implications. Meningitis is associated with many complications and deaths, and the lack of diagnostic methods and accurate prognosis is one of the important challenges in this field. Therefore, identifying and introducing accurate and cheap methods for predicting these complications and taking early measures to prevent the development of complications and treatment can be beneficial. The results of the present study showed that CT scan can be used as an early diagnostic and prognostic method in children with meningitis

Muscular and hepatosplenic candidiasis in a patient with acute myeloblastic leukemia: A case report and literature review

Key Clinical Message Muscular and subcutaneous candidiasis is a rare entity in immunocompromised patients, but it should be kept in mind when we see multiple cystic soft tissue masses in addition to target‐shaped hepatosplenic lesions in neutropenic patients. US and MRI are useful imaging modalities for the diagnosis and follow‐up of these patients. Abstract Soft tissue candidiasis is an opportunistic infection in immunocompromised patients and must always be diagnosed and treated as soon as possible. In this case report, the patient is a 14‐year‐old boy with acute myeloid leukemia M3‐type who presented with numerous soft tissue and hepatosplenic candidal abscesses