A user-friendly deep learning application for accurate lung cancer diagnosis.

BACKGROUND: Accurate diagnosis and subsequent delineated treatment planning require the experience of clinicians in the handling of their case numbers. However, applying deep learning in image processing is useful in creating tools that promise faster high-quality diagnoses, but the accuracy and precision of 3-D image processing from 2-D data may be limited by factors such as superposition of organs, distortion and magnification, and detection of new pathologies. The purpose of this research is to use radiomics and deep learning to develop a tool for lung cancer diagnosis. METHODS: This study applies radiomics and deep learning in the diagnosis of lung cancer to help clinicians accurately analyze the images and thereby provide the appropriate treatment planning. 86 patients were recruited from Bach Mai Hospital, and 1012 patients were collected from an open-source database. First, deep learning has been applied in the process of segmentation by U-NET and cancer classification via the use of the DenseNet model. Second, the radiomics were applied for measuring and calculating diameter, surface area, and volume. Finally, the hardware also was designed by connecting between Arduino Nano and MFRC522 module for reading data from the tag. In addition, the displayed interface was created on a web platform using Python through Streamlit. RESULTS: The applied segmentation model yielded a validation loss of 0.498, a train loss of 0.27, a cancer classification validation loss of 0.78, and a training accuracy of 0.98. The outcomes of the diagnostic capabilities of lung cancer (recognition and classification of lung cancer from chest CT scans) were quite successful. CONCLUSIONS: The model provided means for storing and updating patients' data directly on the interface which allowed the results to be readily available for the health care providers. The developed system will improve clinical communication and information exchange. Moreover, it can manage efforts by generating correlated and coherent summaries of cancer diagnoses.

How much should you worry about contaminant neutrons in spatially fractionated grid radiation therapy?

Neutron contamination in radiation therapy is of concern in treatment with high-energy photons (> 10 MV). With the development of new radiotherapy modalities such as spatially fractionated grid radiation therapy (SFGRT) or briefly grid radiotherapy, more studies are required to evaluate the risks associated with neutron contamination. In 15 MV SFGRT, high-Z materials such as lead and cerrobend are used as the block on the tray of linear accelerator (linac) which can probably increase the photoneutron production. On the other hand, the high-dose fractions (10-20 Gy) used in SFGRT can induce high neutron contamination. The current study was devoted to addressing these concerns via compression of neutron fluence (Φn) and ambient dose equivalent ([Formula: see text]) at the patient table and inside the maze between SFGRT and conventional fractionated radiation therapy (CFRT). The main components of the 15 MV Siemens Primus equipped with different grids and located inside a typical radiotherapy bunker were simulated by the MCNPX® Monte Carlo code. Evidence showed that the material used for grid construction does not significantly increase neutron contamination inside the maze. However, at the end of the maze, neutron contamination in SFGRT is significantly higher than in CFRT. In this regard, a delay time of 15 minutes after SFGRT is recommended for all radiotherapy staff before entering the maze. It can be also concluded that [Formula: see text] at the patient table is at least 10 times more pronounced than inside the maze. Therefore, the patient is more at risk of neutrons compared to the staff. The [Formula: see text] at the isocenter in SFGRT with grids made of lead and cerrobend was nearly equal to CFRT. Nevertheless, it was dramatically lower than in CFRT by 30% if the brass grid is used. Accordingly, SFGRT with the brass grid is recommended, from radiation protection aspects.

Prediction of Normal Tissue Complication Probability (NTCP) After Radiation Therapy Using Imaging and Molecular Biomarkers and Multivariate Modelling.

The aim of this study was to design a predictive radiobiological model of normal brain tissue in low-grade glioma following radiotherapy based on imaging and molecular biomarkers. Fifteen patients with primary brain tumors prospectively participated in this study and underwent radiation therapy. Magnetic resonance imaging (MRI) was obtained from the patients, including T1- and T2-weighted imaging and diffusion tensor imaging (DTI), and a generalized equivalent dose (gEUD) was calculated. The radiobiological model of the normal tissue complication probability (NTCP) was performed using the variables gEUD; axial diffusivity (AD) and radial diffusivity (RD) of the corpus callosum; and serum protein S100B by univariate and multivariate logistic regression XLIIIrd Sir Peter Freyer Memorial Lecture and Surgical Symposium (2018). Changes in AD, RD, and S100B from baseline up to the 6 months after treatment had an increasing trend and were significant in some time points (P-value < 0.05). The model resulting from RD changes in the 6 months after treatment was significantly more predictable of necrosis than other univariate models. The bivariate model combining RD changes in Gy40 dose-volume and gEUD, as well as the trivariate model obtained using gEUD, RD, and S100B, had a higher predictive value among multivariate models at the sixth month of the treatment. Changes in RD diffusion indices and in serum protein S100B value were used in the early-delayed stage as reliable biomarkers for predicting late-delayed damage (necrosis) caused by radiation in the corpus callosum. Current findings could pave the way for intervention therapies to delay the severity of damage to white matter structures, minimize cognitive impairment, and improve the quality of life of patients with low-grade glioma.

PrACTiC: A Predictive Algorithm for Chemoradiotherapy-Induced Cytopenia in Glioblastoma Patients.

BACKGROUND: Chemotherapy-induced cytopenia is the most frequent side effect of chemoradiotherapy in glioblastoma patients which may lead to reduced delivery of treatment. This study aims to develop a predictive model that is able to forecast the cytopenia induced by temozolomide (TMZ) during concomitant chemoradiotherapy. METHODS: Medical records of 128 patients with newly diagnosed glioblastoma were evaluated to extract the baseline complete blood test before and during the six weeks of chemoradiotherapy to create a dataset for the development of ML models. Using the constructed dataset, different ML algorithms were trained and tested. RESULTS: Our proposed algorithm achieved accuracies of 85.6%, 88.7%, and 89.3% in predicting thrombocytopenia, lymphopenia, and neutropenia, respectively. CONCLUSIONS: The algorithm designed and developed in this study, called PrACTiC, showed promising results in the accurate prediction of thrombocytopenia, neutropenia, and lymphopenia induced by TMZ in glioblastoma patients. PrACTiC can provide valuable insight for physicians and help them to make the necessary treatment modifications and prevent the toxicities.

Semi-experimental assessment of neutron equivalent dose and secondary cancer risk for off-field organs in glioma patients undergoing 18-MV radiotherapy.

Neutron contamination as a source of out-of-field dose in radiotherapy is still of concern. High-energy treatment photons have the potential to overcome the binding energy of neutrons inside the nuclei. Fast neutrons emitting from the accelerator head can directly reach the patient's bed. Considering that modern radiotherapy techniques can increase patient survival, concerns about unwanted doses and the lifetime risk of fatal cancer remain strong or even more prominent, especially in young adult patients. The current study addressed these concerns by quantifying the dose and risk of fatal cancer due to photo-neutrons for glioma patients undergoing 18-MV radiotherapy. In this study, an NRD model rem-meter detector was used to measure neutron ambient dose equivalent, H*(10), at the patient table. Then, the neutron equivalent dose received by each organ was estimated concerning the depth of each organ and by applying depth dose corrections to the measured H*(10). Finally, the effective dose and risk of secondary cancer were determined using NCRP 116 coefficients. Evidence revealed that among all organs, the breast (0.62 mSv/Gy) and gonads (0.58 mSv/Gy) are at risk of photoneutrons more than the other organs in such treatments. The neutron effective dose in the 18-MV conventional radiotherapy of the brain was 13.36 mSv. Among all organs, gonads (6.96 mSv), thyroid (1.86 mSv), and breasts (1.86 mSv) had more contribution to the effective dose, respectively. The total secondary cancer risk was estimated as 281.4 cases (per 1 million persons). The highest risk was related to the breast and gonads with 74.4 and, 34.8 cases per 1 million persons, respectively. Therefore, it is recommended that to prevent late complications (secondary cancer and genetic effects), these organs should be shielded from photoneutrons. This procedure not only improves the quality of the patient's personal life but also the healthy childbearing in the community.

Diagnostic and prognostic value of stem cell factor plasma level in glioblastoma multiforme patients.

BACKGROUND: Investigation of novel blood-circulating agents as potential biomarkers for glioblastoma multiforme (GBM) patients' diagnosis and monitoring has gained lots of attention, due to limitations of imaging modalities and invasive tissue biopsy procedures. The present study aims to assess the diagnostic and prognostic values of preoperative stem cell factor (SCF) plasma level in GBM patients. METHODS: Preoperative plasma samples from 58 GBM patients and 20 patients with nonglial tumors and 30 healthy controls were obtained. SCF levels were measured by employing the enzyme-linked immunosorbent assay test and the values were compared between these three groups. Then, the association of SCF plasma level and tumor volume, progression-free survival (PFS), and overall survival (OS) for the GBM patients were evaluated. RESULTS: Mean preoperative SCF plasma level of the GBM patients (2.80 ± 1.52 ng/ml) was significantly higher (p < 0.0001) than the healthy controls (0.80 ± 0.24 ng/ml) and patients with nonglial tumor (1.41 ± 0.76 ng/ml). Receiver operating characteristic analysis revealed that the preoperative SCF plasma level could distinguish the GBM patients from healthy controls and patients with nonglial tumors with the area under curve values of 0.915 and 0.790, respectively. However, no significant association was observed between the GBM patients' preoperative SCF plasma levels and tumors' volume (Spearman Rho correlation coefficient, 0.1847; 95% CI, p = 0.1652). The GBM patients were divided into two subgroups based on mean preoperative SCF plasma levels (2.80 ng/ml). No significant difference was observed between the patients' PFS (p = 0.3792) and OS (p = 0.1469) at these two subgroups. CONCLUSION: Taking together, the SCF plasma level can serve as a novel diagnostic blood-circulating biomarker for patients with GBM. However, its plasma level is not correlated with GBM patients' tumor volume, PFS, or OS.

Neurofilament light chain as a biomarker for diagnosis of multiple sclerosis.

The treatments for multiple sclerosis (MS) have improved over the past 25 years, but now the main question for physicians is deciding who should receive treatment, for how long, and when to switch to other options. These decisions are typically based on treatment tolerance and a reasonable expectation of long-term efficacy. A significant unmet need is the lack of accurate laboratory measurements for diagnosis, and monitoring of treatment response, including deterioration and disease progression. There are few validated biomarkers for MS, and in practice, physicians employ two biomarkers discovered fifty years ago for MS diagnosis, often in combination with MRI scans. These biomarkers are intrathecal IgG and oligoclonal bands in the CSF (cerebrospinal fluid). Neurofilament light chain (NfL) is a relatively new biomarker for MS diagnosis and follow up. Neurofilaments are neuron-specific cytoskeleton proteins that can be measured in various body compartments. NfL is a new biomarker for MS that can be measured in serum samples, but this still needs further study to specify the laboratory cut-off values in clinical practice. In the present review we discuss the evidence for NfL as a reliable biomarker for the early detection and management of MS. Moreover, we highlight the correlation between MRI and NfL, and ask whether they can be combined.

Early Detection of Radiation-Induced Injury and Prediction of Cognitive Deficit by MRS Metabolites in Radiotherapy of Low-Grade Glioma.

PURPOSE: To compare the sensitivity of MRS metabolites and MoCA and ACE-R cognitive tests in the detection of radiation-induced injury in low grade glioma (LGG) patients in early and early delayed postradiation stages. METHODS: MRS metabolite ratios of NAA/Cr and Cho/Cr, ACE-R and MoCA cognitive tests, and dosimetric parameters in corpus callosum were analyzed during RT and up to 6-month post-RT for ten LGG patients. RESULTS: Compared to pre RT baseline, a significant decline in both NAA/Cr and Cho/Cr in the corpus callosum was seen at the 4th week of RT, 1, 3, and 6-month post-RT. These declines were detected at least 3 months before the detection of declines in cognitive functions by ACE-R and MoCA tools. Moreover, NAA/Cr alterations at 4th week of RT and 1-month post-RT were significantly negatively correlated with the mean dose received by the corpus callosum, as well as the corpus callosum 40 Gy dose volume, i.e., the volume of the corpus callosum receiving a dose greater than 40 Gy. CONCLUSION: MRS-based biomarkers may be more sensitive than the state-of-the-art cognitive tests in the prediction of postradiation cognitive impairments. They would be utilized in treatment planning and dose sparing protocols, with a specific focus on the corpus callosum in the radiation therapy of LGG patients.

Monte Carlo evaluation of out-of-field dose in 18 MV pelvic radiotherapy using a simplified female MIRD phantom.

This study was devoted to determining the unwanted dose due to scattered photons to the out-of-field organs and subsequently estimate the risk of secondary cancers in the patients undergoing pelvic radiotherapy. A typical 18 MV Medical Linear Accelerator (Varian Clinac 2100 C/D) was modeled using MCNPX®code to simulate pelvic radiotherapy with four treatment fields: anterior-posterior, posterior-anterior, right lateral, left lateral. Dose evaluation was performed inside Medical Internal Radiation Dose (MIRD) revised female phantom. The average photon equivalent dose in out-of-field organs is 8.53 mSv Gy-1, ranging from 0.17 to 72.11 mSv Gy-1, respectively, for the organs far from the Planning Treatment Volume (Brain) and those close to the treatment field (Colon). Evidence showed that colon with 4.3049% and thyroid with 0.0020% have the highest and lowest risk of secondary cancer, respectively. Accordingly, this study introduced the colon as an organ with a high risk of secondary cancer which should be paid more attention in the follow-up of patients undergoing pelvic radiotherapy. The authors believe that this simple Monte Carlo (MC) model can be also used in other radiotherapy plans and mathematical phantoms with different ages (from childhood to adults) to estimate the out-of-field dose. The extractable information by this simple MC model can be also employed for providing libraries for user-friendly applications (e.g. '.apk') which in turn increase the public knowledge about fatal cancer risk after radiotherapy and subsequently decrease the concerns in this regard among the public.

Joint application of biochemical markers and imaging techniques in the accurate and early detection of glioblastoma.

Glioblastoma is a primary brain tumor with the most metastatic effect in adults. Despite the wide range of multidimensional treatments, tumor heterogeneity is one of the main causes of tumor spread and gives great complexity to diagnostic and therapeutic methods. Therefore, featuring noble noninvasive prognostic methods that are focused on glioblastoma heterogeneity is perceived as an urgent need. Imaging neuro-oncological biomarkers including MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status, tumor grade along with other tumor characteristics and demographic features (e.g., age) are commonly referred to during diagnostic, therapeutic and prognostic processes. Therefore, the use of new noninvasive prognostic methods focused on glioblastoma heterogeneity is considered an urgent need. Some neuronal biomarkers, including the promoter methylation status of the promoter MGMT, the characteristics and grade of the tumor, along with the patient's demographics (such as age and sex) are involved in diagnosis, treatment, and prognosis. Among the wide array of imaging techniques, magnetic resonance imaging combined with the more physiologically detailed technique of H-magnetic resonance spectroscopy can be useful in diagnosing neurological cancer patients. In addition, intracranial tumor qualitative analysis and sometimes tumor biopsies help in accurate diagnosis. This review summarizes the evidence for biochemical biomarkers being a reliable biomarker in the early detection and disease management in GBM. Moreover, we highlight the correlation between Imaging techniques and biochemical biomarkers and ask whether they can be combined.

Tunable spectral shifts and spectral switches by controllable phase modulation.

In this work, we offer a novel and flexible approach of spectral switches which can be handled more simply by controlling the phase of the diffracted light field of a completely spatially coherent incident beam with spectral profile from a one-dimensional phase step. This scheme has the benefit of easy implementation by simply varying the height of a one-dimensional phase step which causes spectral switches to occur when the step height reaches certain critical values without modulating any properties of the light source. To illustrate this effect, an explicit and analytical expression at an observation point corresponding to the step edge is obtained and some numerical examples are given and examined experimentally. Finally, based on the obtained results, it is shown that this method with the capability of very short response time can be easily applied to information encoding and transmission.

Neurofilament Light Chain as a Biomarker, and Correlation with Magnetic Resonance Imaging in Diagnosis of CNS-Related Disorders.

The search for diagnostic and prognostic biomarkers for neurodegenerative conditions is of high importance, since these disorders may present difficulties in differential diagnosis. Biomarkers with high sensitivity and specificity are required. Neurofilament light chain (NfL) is a unique biomarker related to axonal damage and neural cell death, which is elevated in a number of neurological disorders, and can be detected in cerebrospinal fluid (CSF), as well as blood, serum, or plasma samples. Although the NfL concentration in CSF is higher than that in blood, blood measurement may be easier in practice due to its lesser invasiveness, reproducibility, and convenience. Many studies have investigated NfL in both CSF and serum/plasma as a potential biomarker of neurodegenerative disorders. Neuroimaging biomarkers can also potentially improve detection of CNS-related disorders at an early stage. Magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) are sensitive techniques to visualize neuroaxonal loss. Therefore, investigating the combination of NfL levels with indices extracted from MRI and DTI scans could potentially improve diagnosis of CNS-related disorders. This review summarizes the evidence for NfL being a reliable biomarker in the early detection and disease management in several CNS-related disorders. Moreover, we highlight the correlation between MRI and NfL and ask whether they can be combined.

Assessment of Effective Dose Associated with Coronary Computed Tomography Angiography in Isfahan Province, Iran.

Computed tomography coronary angiography (CTCA) has generated a great interest over the past two decades, due to its high diagnostic accuracy and efficacy in the assessment of patients having coronary artery disease. This method is associated with high radiation dose and this has raised serious concerns in the literature. Effective dose (E) is a single parameter meant to reflect the relative risk from exposure to ionizing radiation. Therefore, it is necessary to calculate this parameter to indicate ionizing radiation relative risk. The aim of this study was to calculate the effective dose from 64-slice CTCA in Isfahan. To calculate the effective dose, an ionization chamber and a body phantom with diameter of 32 cm and length of 15 cm were used. CTCA radiation conditions commonly used in two centers were applied for this work. For all scans, computed tomography volume dose index (CTDIv), dose-length product (DLP), and effective dose were obtained using dose-length-product method. The obtained CTDIv, DLP, and effective dose were compared in two centers, and mean, maximum, and minimum values of effective dose for heart coronary CT angiography (CCTA) examinations and calcium score were compared with other studies. The amount of average, maximum, and minimum effective doses for heart CCTA examinations in two centers are 4.65 ± 0.06, 6.0489, and 3.492 mSv, respectively, and for calcium score test are, 1.04 ± 0.04, 2.155, and 0.98 mSv, respectively. CTDIv, DLP, and effective dose values did not show any significant difference in two centers. Although the effective dose of CTCA and calcium score was lower than that of other studies, it is reasonable to reduce the effective dose to the minimum possible value to reduce the risk of cancer associated with ionizing radiation. The results of this study can be used to introduce the effective dose as a local diagnostic reference dose (DRL) for CTCA examinations in Isfahan Province.

Electromagnetic fields and optomechanics in cancer diagnostics and treatment.

In this paper, we discuss biological effects of electromagnetic (EM) fields in the context of cancer biology. In particular, we review the nanomechanical properties of microtubules (MTs), the latter being one of the most successful targets for cancer therapy. We propose an investigation on the coupling of electromagnetic radiation to mechanical vibrations of MTs as an important basis for biological and medical applications. In our opinion, optomechanical methods can accurately monitor and control the mechanical properties of isolated MTs in a liquid environment. Consequently, studying nanomechanical properties of MTs may give useful information for future applications to diagnostic and therapeutic technologies involving non-invasive externally applied physical fields. For example, electromagnetic fields or high intensity ultrasound can be used therapeutically avoiding harmful side effects of chemotherapeutic agents or classical radiation therapy.