Second malignancies in patients with primary central nervous system lymphoma

Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks

Brain cancer is a disease caused by the growth of abnormal aggressive cells in the brain outside of normal cells. Symptoms and diagnosis of brain cancer cases are producing more accurate results day by day in parallel with the development of technological opportunities. In this study, a deep learning model called BrainMRNet which is developed for mass detection in open-source brain magnetic resonance images was used. The BrainMRNet model includes three processing steps: attention modules, the hypercolumn technique, and residual blocks. To demonstrate the accuracy of the proposed model, three types of tumor data leading to brain cancer were examined in this study: glioma, meningioma, and pituitary. In addition, a segmentation method was proposed, which additionally determines in which lobe area of the brain the two classes of tumors that cause brain cancer are more concentrated. The classification accuracy rates were performed in the study; it was 98.18% in glioma tumor, 96.73% in meningioma tumor, and 98.18% in pituitary tumor. At the end of the experiment, using the subset of glioma and meningioma tumor images, it was determined which at brain lobe the tumor region was seen, and 100% success was achieved in the analysis of this determination. In this study, a hybrid deep learning model is presented to determine the detection of the brain tumor. In addition, open-source software was proposed, which statistically found in which lobe region of the human brain the brain tumor occurred. The methods applied and tested in the experiments have shown promising results with a high level of accuracy, precision, and specificity. These results demonstrate the availability of the proposed approach in clinical settings to support the medical decision regarding brain tumor detection.

Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks

Brain cancer is a disease caused by the growth of abnormal aggressive cells in the brain outside of normal cells. Symptoms and diagnosis of brain cancer cases are producing more accurate results day by day in parallel with the development of technological opportunities. In this study, a deep learning model called BrainMRNet which is developed for mass detection in open-source brain magnetic resonance images was used. The BrainMRNet model includes three processing steps: attention modules, the hypercolumn technique, and residual blocks. To demonstrate the accuracy of the proposed model, three types of tumor data leading to brain cancer were examined in this study: glioma, meningioma, and pituitary. In addition, a segmentation method was proposed, which additionally determines in which lobe area of the brain the two classes of tumors that cause brain cancer are more concentrated. The classification accuracy rates were performed in the study; it was 98.18% in glioma tumor, 96.73% in meningioma tumor, and 98.18% in pituitary tumor. At the end of the experiment, using the subset of glioma and meningioma tumor images, it was determined which at brain lobe the tumor region was seen, and 100% success was achieved in the analysis of this determination. In this study, a hybrid deep learning model is presented to determine the detection of the brain tumor. In addition, open-source software was proposed, which statistically found in which lobe region of the human brain the brain tumor occurred. The methods applied and tested in the experiments have shown promising results with a high level of accuracy, precision, and specificity. These results demonstrate the availability of the proposed approach in clinical settings to support the medical decision regarding brain tumor detection.

Comprehensive approach to diagnosis and treatment of newly diagnosed primary CNS lymphoma

Primary central nervous system lymphoma (PCNSL) is a rare form of non-Hodgkin lymphoma that affects the brain parenchyma, spinal cord, eyes, and cerebrospinal fluid without evidence of systemic, non-CNS involvement. PCNSL is uncommon and only a few randomized trials have been completed in the first-line setting. Over the past decades, the prognosis of PCNSL has improved, mainly due to the introduction and widespread use of high-dose methotrexate, which is now the backbone of all first-line treatment polychemotherapy regimens. Despite this progress, durable remission is recorded in only 50% of patients, and therapy can be associated with significant late neurotoxicity. Here, we overview the epidemiology, clinical presentation, staging evaluation, prognosis, and current up-to-date treatment of immunocompetent PCNSL patients.

Real-World Impact of Surgical Excision on Overall Survival in Primary Central Nervous System Lymphoma

Substantial controversy still exists regarding the use of surgical excision in the treatment of primary central nervous system lymphoma (PCNSL). This study was aimed to evaluate the survival benefit of surgical excision in PCNSL patients based on a US population. Using the Surveillance, Epidemiology, and End Results (SEER) Program database, a total of 3,543 PCNSL patients were identified from 2000 to 2014 for analysis. Surgical excision was accessed via Kaplan–Meier and multivariate Cox regression survival analyses. Coarsened exact matching (CEM) analysis was additionally employed to intensify our findings. Finally, we found that surgical excision was significantly associated with increased survival over no surgery/biopsy (P < 0.001), and its survival benefit was also independent of baseline prognostic factors. The survival benefit of surgery was also validated in clinically important subsets. CEM analysis further validated the survival advantage of surgery (P < 0.001). Moreover, a novel prediction model with excellent performance was established to estimate the potential benefit from surgical excision of the lesion with respect to the end point of overall survival. The current study supports the favorable impact of surgical excision on clinical outcome in patients with PCNSL. Although further randomized controlled trials are warranted, currently available evidence should be considered in the clinical management of this disease.

Modeling age-dependent radiation-induced second cancer risks and estimation of mutation rate: an evolutionary approach

Although the survival rate of cancer patients has significantly increased due to advances in anti-cancer therapeutics, one of the major side effects of these therapies, particularly radiotherapy, is the potential manifestation of radiation-induced secondary malignancies. In this work, a novel evolutionary stochastic model is introduced that couples short-term formalism (during radiotherapy) and long-term formalism (post-treatment). This framework is used to estimate the risks of second cancer as a function of spontaneous background and radiation-induced mutation rates of normal and pre-malignant cells. By fitting the model to available clinical data for spontaneous background risk together with data of Hodgkin's lymphoma survivors (for various organs), the second cancer mutation rate is estimated. The model predicts a significant increase in mutation rate for some cancer types, which may be a sign of genomic instability. Finally, it is shown that the model results are in agreement with the measured results for excess relative risk (ERR) as a function of exposure age and that the model predicts a negative correlation of ERR with increase in attained age. This novel approach can be used to analyze several radiotherapy protocols in current clinical practice and to forecast the second cancer risks over time for individual patients.