Structural variations in parotid glands induced by radiation therapy in patients with oral carcinoma observed on contrast-enhanced computed tomography

Background: Xerostomia is one of the commonest radiation-induced complications in patients with head and neck carcinoma. The aim of this study was to assess structural variations in parotid glands induced by radiation therapy in patients with oral carcinoma with contras-enhanced computed tomography (CECT). Material/Methods: A retrospective study was performed in 41 patients with oral carcinoma who underwent CECT for head and neck malignancies before and after radiotherapy. We analyzed the relationship between parotid density variations, parotid volume change, as seen on CECT, and the mean radiation dose applied to the parotid glands in patients with oral carcinoma immediately after radiotherapy, and 2 and 3 years later. Results: Immediately after radiotherapy, high-density changes on contrast-enhanced CT were observed in 70.5% of the irradiated parotids. Low-density changes due to fat degeneration were seen in 46.2% and 72.2% of the irradiated parotids 2 and 3 years after radiotherapy, respectively. The mean dose applied to the parotids with the low-density changes and without such changes 3 years after radiotherapy was 46.0 Gy and 27.7 Gy, respectively (p=0.049). Furthermore, parotid shrinkage was observed in 63.6% of the irradiated parotids. Conclusions: This study suggests that the structural variations in parotid glands induced by radiotherapy included high-density changes that were observed immediately after radiotherapy and low-density changes that were seen at late follow-up. This study should be useful for clinicians in the assessment of radiation-induced injuries in the parotids with respect to early prediction of xerostomia

Investigation of Radiation-Induced Toxicity in Head and Neck Cancer Patients through Radiomics and Machine Learning: A Systematic Review

Background. Radiation-induced toxicity represents a crucial concern in oncological treatments of patients affected by head and neck neoplasms, due to its impact on survivors' quality of life. Published reports suggested the potential of radiomics combined with machine learning methods in the prediction and assessment of radiation-induced toxicities, supporting a tailored radiation treatment management. In this paper, we present an update of the current knowledge concerning these modern approaches. Materials and Methods. A systematic review according to PICO-PRISMA methodology was conducted in MEDLINE/PubMed and EMBASE databases until June 2019. Studies assessing the use of radiomics combined with machine learning in predicting radiation-induced toxicity in head and neck cancer patients were specifically included. Four authors (two independently and two in concordance) assessed the methodological quality of the included studies using the Radiomic Quality Score (RQS). The overall score for each analyzed study was obtained by the sum of the single RQS items; the average and standard deviation values of the authors' RQS were calculated and reported. Results. Eight included papers, presenting data on parotid glands, cochlea, masticatory muscles, and white brain matter, were specifically analyzed in this review. Only one study had an average RQS was ≤ 30% (50%), while 3 studies obtained a RQS almost ≤ 25%. Potential variability in the interpretations of specific RQS items could have influenced the inter-rater agreement in specific cases. Conclusions. Published radiomic studies provide encouraging but still limited and preliminary data that require further validation to improve the decision-making processes in preventing and managing radiation-induced toxicities

Multi-Modality Automatic Lung Tumor Segmentation Method Using Deep Learning and Radiomics

Delineation of the tumor volume is the initial and fundamental step in the radiotherapy planning process. The current clinical practice of manual delineation is time-consuming and suffers from observer variability. This work seeks to develop an effective automatic framework to produce clinically usable lung tumor segmentations. First, to facilitate the development and validation of our methodology, an expansive database of planning CTs, diagnostic PETs, and manual tumor segmentations was curated, and an image registration and preprocessing pipeline was established. Then a deep learning neural network was constructed and optimized to utilize dual-modality PET and CT images for lung tumor segmentation. The feasibility of incorporating radiomics and other mechanisms such as a tumor volume-based stratification scheme for training/validation/testing were investigated to improve the segmentation performance. The proposed methodology was evaluated both quantitatively with similarity metrics and clinically with physician reviews. In addition, external validation with an independent database was also conducted. Our work addressed some of the major limitations that restricted clinical applicability of the existing approaches and produced automatic segmentations that were consistent with the manually contoured ground truth and were highly clinically-acceptable according to both the quantitative and clinical evaluations. Both novel approaches of implementing a tumor volume-based training/validation/ testing stratification strategy as well as incorporating voxel-wise radiomics feature images were shown to improve the segmentation performance. The results showed that the proposed method was effective and robust, producing automatic lung tumor segmentations that could potentially improve both the quality and consistency of manual tumor delineation

Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty

ADVANCED IMAGING ANALYSIS FOR PREDICTING TUMOR RESPONSE AND IMPROVING CONTOUR DELINEATION UNCERTAINTY By Rebecca Nichole Mahon, MS A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2018 Major Director: Dr. Elisabeth Weiss, Professor, Department of Radiation Oncology Radiomics, an advanced form of imaging analysis, is a growing field of interest in medicine. Radiomics seeks to extract quantitative information from images through use of computer vision techniques to assist in improving treatment. Early prediction of treatment response is one way of improving overall patient care. This work seeks to explore the feasibility of building predictive models from radiomic texture features extracted from magnetic resonance (MR) and computed tomography (CT) images of lung cancer patients. First, repeatable primary tumor texture features from each imaging modality were identified to ensure a sufficient number of repeatable features existed for model development. Then a workflow was developed to build models to predict overall survival and local control using single modality and multi-modality radiomics features. The workflow was also applied to normal tissue contours as a control study. Multiple significant models were identified for the single modality MR- and CT-based models, while the multi-modality models were promising indicating exploration with a larger cohort is warranted. Another way advances in imaging analysis can be leveraged is in improving accuracy of contours. Unfortunately, the tumor can be close in appearance to normal tissue on medical images creating high uncertainty in the tumor boundary. As the entire defined target is treated, providing physicians with additional information when delineating the target volume can improve the accuracy of the contour and potentially reduce the amount of normal tissue incorporated into the contour. Convolution neural networks were developed and trained to identify the tumor interface with normal tissue and for one network to identify the tumor location. A mock tool was presented using the output of the network to provide the physician with the uncertainty in prediction of the interface type and the probability of the contour delineation uncertainty exceeding 5mm for the top three predictions

Smoke, alcohol and drug addiction and male fertility

In recent decades, the decline in human fertility has become increasingly more worrying: while therapeutic interventions might help, they are vexing for the couple and often burdened with high failure rates and costs. Prevention is the most successful approach to fertility disorders in males and females alike. We performed a literature review on three of the most common unhealthy habits - tobacco, alcohol and drug addiction - and their reported effects on male fertility. Tobacco smoking is remarkably common in most first-world countries; despite a progressive decline in the US, recent reports suggest a prevalence of more than 30% in subjects of reproductive age - a disturbing perspective, given the well-known ill-effects on reproductive and sexual function as well as general health. Alcohol consumption is often considered socially acceptable, but its negative effects on gonadal function have been consistently reported in the last 30 years. Several studies have reported a variety of negative effects on male fertility following drug abuse - a worrying phenomenon, as illicit drug consumption is on the rise, most notably in younger subjects. While evidence in these regards is still far from solid, mostly as a result of several confounding factors, it is safe to assume that cessation of tobacco smoking, alcohol consumption and recreational drug addiction might represent the best course of action for any couple trying to achieve pregnancy

Theranostics in Boron neutron capture therapy

Boron neutron capture therapy (BNCT) has the potential to specifically destroy tumor cells without damaging the tissues infiltrated by the tumor. BNCT is a binary treatment method based on the combination of two agents that have no effect when applied individually:10B and thermal neutrons. Exclusively, the combination of both produces an effect, whose extent depends on the amount of10B in the tumor but also on the organs at risk. It is not yet possible to determine the10B concentration in a specific tissue using non-invasive methods. At present, it is only possible to measure the10B concentration in blood and to estimate the boron concentration in tissues based on the assumption that there is a fixed uptake of10B from the blood into tissues. On this imprecise assumption, BNCT can hardly be developed further. A therapeutic approach, combining the boron carrier for therapeutic purposes with an imaging tool, might allow us to determine the10B concentration in a specific tissue using a non-invasive method. This review provides an overview of the current clinical protocols and preclinical experiments and results on how innovative drug development for boron delivery systems can also incorporate concurrent imaging. The last section focuses on the importance of proteomics for further optimization of BNCT, a highly precise and personalized therapeutic approach

Theranostics in Boron Neutron Capture Therapy

Boron neutron capture therapy (BNCT) has the potential to specifically destroy tumor cells without damaging the tissues infiltrated by the tumor. BNCT is a binary treatment method based on the combination of two agents that have no effect when applied individually: B-10 and thermal neutrons. Exclusively, the combination of both produces an effect, whose extent depends on the amount of B-10 in the tumor but also on the organs at risk. It is not yet possible to determine the B-10 concentration in a specific tissue using non-invasive methods. At present, it is only possible to measure the B-10 concentration in blood and to estimate the boron concentration in tissues based on the assumption that there is a fixed uptake of B-10 from the blood into tissues. On this imprecise assumption, BNCT can hardly be developed further. A therapeutic approach, combining the boron carrier for therapeutic purposes with an imaging tool, might allow us to determine the B-10 concentration in a specific tissue using a non-invasive method. This review provides an overview of the current clinical protocols and preclinical experiments and results on how innovative drug development for boron delivery systems can also incorporate concurrent imaging. The last section focuses on the importance of proteomics for further optimization of BNCT, a highly precise and personalized therapeutic approach

Pseudoprogression of brain tumors

This review describes the definition, incidence, clinical implications, and magnetic resonance imaging (MRI) findings of pseudoprogression of brain tumors, in particular, but not limited to, high-grade glioma. Pseudoprogression is an important clinical problem after brain tumor treatment, interfering not only with day-to-day patient care but also the execution and interpretation of clinical trials. Radiologically, pseudoprogression is defined as a new or enlarging area(s) of contrast agent enhancement, in the absence of true tumor growth, which subsides or stabilizes without a change in therapy. The clinical definitions of pseudoprogression have been quite variable, which may explain some of the differences in reported incidences, which range from 9-30%. Conventional structural MRI is insufficient for distinguishing pseudoprogression from true progressive disease, and advanced imaging is needed to obtain higher levels of diagnostic certainty. Perfusion MRI is the most widely used imaging technique to diagnose pseudoprogression and has high reported diagnostic accuracy. Diagnostic performance of MR spectroscopy (MRS) appears to be somewhat higher, but MRS is less suitable for the routine and universal application in brain tumor follow-up. The combination of MRS and diffusion-weighted imaging and/or perfusion MRI seems to be particularly powerful, with diagnostic accuracy reaching up to or even greater than 90%. While diagnostic performance can be high with appropriate implementation and interpretation, even a combination of techniques, however, does not provide 100% accuracy. It should also be noted that most studies to date are small, heterogeneous, and retrospective in nature. Future improvements in diagnostic accuracy can be expected with harmonization of acquisition and postprocessing, quantitative MRI and computer-aided diagnostic technology, and meticulous evaluation with clinical and pathological data

Theranostics in Boron Neutron Capture Therapy

Boron neutron capture therapy (BNCT) has the potential to specifically destroy tumor cells without damaging the tissues infiltrated by the tumor. BNCT is a binary treatment method based on the combination of two agents that have no effect when applied individually: 10B and thermal neutrons. Exclusively, the combination of both produces an effect, whose extent depends on the amount of 10B in the tumor but also on the organs at risk. It is not yet possible to determine the 10B concentration in a specific tissue using non-invasive methods. At present, it is only possible to measure the 10B concentration in blood and to estimate the boron concentration in tissues based on the assumption that there is a fixed uptake of 10B from the blood into tissues. On this imprecise assumption, BNCT can hardly be developed further. A therapeutic approach, combining the boron carrier for therapeutic purposes with an imaging tool, might allow us to determine the 10B concentration in a specific tissue using a non-invasive method. This review provides an overview of the current clinical protocols and preclinical experiments and results on how innovative drug development for boron delivery systems can also incorporate concurrent imaging. The last section focuses on the importance of proteomics for further optimization of BNCT, a highly precise and personalized therapeutic approach

Theranostics in Boron neutron capture therapy

Boron neutron capture therapy (BNCT) has the potential to specifically destroy tumor cells without damaging the tissues infiltrated by the tumor. BNCT is a binary treatment method based on the combination of two agents that have no effect when applied individually:B and thermal neutrons. Exclusively, the combination of both produces an effect, whose extent depends on the amount ofB in the tumor but also on the organs at risk. It is not yet possible to determine theB concentration in a specific tissue using non-invasive methods. At present, it is only possible to measure theB concentration in blood and to estimate the boron concentration in tissues based on the assumption that there is a fixed uptake ofB from the blood into tissues. On this imprecise assumption, BNCT can hardly be developed further. A therapeutic approach, combining the boron carrier for therapeutic purposes with an imaging tool, might allow us to determine theB concentration in a specific tissue using a non-invasive method. This review provides an overview of the current clinical protocols and preclinical experiments and results on how innovative drug development for boron delivery systems can also incorporate concurrent imaging. The last section focuses on the importance of proteomics for further optimization of BNCT, a highly precise and personalized therapeutic approach.E.H.-H. and M.K. gratefully acknowledge support from the DFG (HE 1376/38-1); L.S. received funding from GEFLUC Grenoble Dauphiné Savoie