Search

Since ancient times, humans have learned to use fire and other heating methods to fight against dangerous pathogens, like cooking raw food, sterilizing surgical tools, and disinfecting other pathogen transmission media. However, it remains difficult for current heating methods to achieve extremely fast and highly efficient sterilization simultaneously. Herein, an ultrafast and uniform heating‐based strategy with outstanding bactericidal performance is proposed. Ultra‐precise laser manufacturing is used to fabricate the Joule heater which can be rapidly heated to 90 °C in 5 s with less than 1 °C fluctuation in a large area by real‐time temperature feedback control. An over 98% bactericidal efficiency on S. aureus for 30 s and on E. coli for merely 5 s is shown. The heating strategy shows a 360 times faster acceleration compared to the commonly used steam sterilization from the suggested guidelines by the Centers for Disease Control and Prevention (CDC), indicating that high temperatures with short duration can effectively disinfect microorganisms. As a proof of concept, this heating strategy can be widely applied to sterilizing cash and various objects to help protect the public from bacteria in daily life. [ABSTRACT FROM AUTHOR]

Background: Current methods for Gamma Knife (GK) treatment planning utilizes either manual forward planning, where planners manually place shots in a tumor to achieve a desired dose distribution, or inverse planning, whereby the dose delivered to a tumor is optimized for multiple objectives based on established metrics. For other treatment modalities like IMRT and VMAT, there has been a recent push to develop knowledge‐based planning (KBP) pipelines to address the limitations presented by forward and inverse planning. However, no complete KBP pipeline has been created for GK. Purpose: To develop a novel (KBP) pipeline, using inverse optimization (IO) with 3D dose predictions for GK. Methods: Data were obtained for 349 patients from Sunnybrook Health Sciences Centre. A 3D dose prediction model was trained using 322 patients, based on a previously published deep learning methodology, and dose predictions were generated for the remaining 27 out‐of‐sample patients. A generalized IO model was developed to learn objective function weights from dose predictions. These weights were then used in an inverse planning model to generate deliverable treatment plans. A dose mimicking (DM) model was also implemented for comparison. The quality of the resulting plans was compared to their clinical counterparts using standard GK quality metrics. The performance of the models was also characterized with respect to the dose predictions. Results: Across all quality metrics, plans generated using the IO pipeline performed at least as well as or better than the respective clinical plans. The average conformity and gradient indices of IO plans was 0.737 ±$\pm$ 0.158 and 3.356 ±$\pm$ 1.030 respectively, compared to 0.713 ±$\pm$ 0.124 and 3.452 ±$\pm$ 1.123 for the clinical plans. IO plans also performed better than DM plans for five of the six quality metrics. Plans generated using IO also have average treatment times comparable to that of clinical plans. With regards to the dose predictions, predictions with higher conformity tend to result in higher quality KBP plans. Conclusions: Plans resulting from an IO KBP pipeline are, on average, of equal or superior quality compared to those obtained through manual planning. The results demonstrate the potential for the use of KBP to generate GK treatment with minimal human intervention. [ABSTRACT FROM AUTHOR]

The progression of cancer involves a critical step in which malignant cells escape from control by the immune system. Antineoplastic agents are particularly efficient when they succeed in restoring such control (immunosurveillance) or at least establish an equilibrium state that slows down disease progression. This is true not only for immunotherapies, such as immune checkpoint inhibitors (ICIs), but also for conventional chemotherapy, targeted anticancer agents, and radiation therapy. Thus, therapeutics that stress and kill cancer cells while provoking a tumor-targeting immune response, referred to as immunogenic cell death, are particularly useful in combination with ICIs. Modern oncology regimens are increasingly using such combinations, which are referred to as chemoimmunotherapy, as well as combinations of multiple ICIs. However, the latter are generally associated with severe side effects compared with single-agent ICIs. Of note, the success of these combinatorial strategies against locally advanced or metastatic cancers is now spurring successful attempts to move them past the postoperative (adjuvant) setting to the preoperative (neoadjuvant) setting, even for patients with operable cancers. Here, the authors critically discuss the importance of immunosurveillance in modern clinical cancer management. [ABSTRACT FROM AUTHOR]

We study the potential negative impact of imbalanced compensation schemes on firm performance. We analyze data from a radiology workflow platform that connects off‐site radiologists with hospitals. These radiologists select tasks from a common pool, while service level is defined by priority‐specific turnaround time targets. However, imbalances between pay and workload of different tasks could result in higher priority tasks with low pay‐to‐workload ratio receiving poorer service. We investigate this hypothesis, showing turnaround time is decreasing in pay‐to‐workload for lower priority tasks, whereas it is increasing in workload for high‐priority tasks. Crucially, we find evidence of an externality effect: Having many economically attractive tasks with low priority can lead to longer turnaround times for higher priority tasks, increasing their likelihood of delay, thus partially defeating the purpose of the priority classes. Highlights: It has been studied that salaried hospital radiologists working on emergency images tend to sequence images to minimize their work queue. However, the advent of new radiology workflow platforms that aggregate images from multiple hospitals, where independent radiologists choose which emergency and nonemergency images to read, creates novel operational challenges that are becoming pervasive in the sharing economy.We find that independent radiologists behave as previously established when reading emergency images. However, for nonemergency images, they prefer to process images that provide a higher bang‐for‐the‐buck, meaning they lead to a higher monetary payout for the time spent on processing.Crucially, we also find that this preference exerts a negative externality on nonemergency images with high‐operational priority (for example, that can generate bed‐blocking), resulting in longer turnaround times and a higher frequency of delay.The delayed processing exerts a bed‐blocking and patient dissatisfaction cost on client hospitals. We suggest radiology workflow platforms use their information systems capabilities to hide nonemergency, high bang‐for‐the‐buck images from radiologists until the higher priority studies are cleared out. [ABSTRACT FROM AUTHOR]

One of the selling points of cryptoassets has been the ability to subject them to so‐called 'smart contracts' embedded upon blockchains; yet, despite numerous common law decisions accepting cryptoassets as property, until Janesh s/o Rajkumar v Unknown Person ('CHEFPIERRE') no courts have had the occasion to consider how such property (in this case, an NFT) interact with these 'smart contracts'. The case considers 'smart contracts' in the context of decentralised finance (DeFi), thus also raising questions concerning the legal effectiveness and prudence of using cryptoassets as objects of security. Although the non‐participation of the defendant meant that the court was deprived of full arguments, the judgment remains worthy of consideration, both for what the court does consider – specifically, criticisms of the Ainsworth test of property – and what it does not. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is especially severe in aged patients, defined as 65 years or older, for reasons that are currently unknown. To investigate the underlying basis for this vulnerability, we performed multimodal data analyses on immunity, inflammation, and COVID-19 incidence and severity as a function of age. Our analysis leveraged age-specific COVID-19 mortality and laboratory testing from a large COVID-19 registry, along with epidemiological data of ~3.4 million individuals, large-scale deep immune cell profiling data, and single-cell RNA-sequencing data from aged COVID-19 patients across diverse populations. We found that decreased lymphocyte count and elevated inflammatory markers (C-reactive protein, D-dimer, and neutrophil-lymphocyte ratio) are significantly associated with age-specific COVID-19 severities. We identified the reduced abundance of naïve CD8 T cells with decreased expression of antiviral defense genes (i.e., IFITM3 and TRIM22) in aged severe COVID-19 patients. Older individuals with severe COVID-19 displayed type I and II interferon deficiencies, which is correlated with SARS-CoV-2 viral load. Elevated expression of SARS-CoV-2 entry factors and reduced expression of antiviral defense genes (LY6E and IFNAR1) in the secretory cells are associated with critical COVID-19 in aged individuals. Mechanistically, we identified strong TGF-beta-mediated immune-epithelial cell interactions (i.e., secretory-non-resident macrophages) in aged individuals with critical COVID-19. Taken together, our findings point to immuno-inflammatory factors that could be targeted therapeutically to reduce morbidity and mortality in aged COVID-19 patients. [ABSTRACT FROM AUTHOR]

Purpose: To advance fair and consistent comparisons of dose prediction methods for knowledge‐based planning (KBP) in radiation therapy research. Methods: We hosted OpenKBP, a 2020 AAPM Grand Challenge, and challenged participants to develop the best method for predicting the dose of contoured computed tomography (CT) images. The models were evaluated according to two separate scores: (a) dose score, which evaluates the full three‐dimensional (3D) dose distributions, and (b) dose‐volume histogram (DVH) score, which evaluates a set DVH metrics. We used these scores to quantify the quality of the models based on their out‐of‐sample predictions. To develop and test their models, participants were given the data of 340 patients who were treated for head‐and‐neck cancer with radiation therapy. The data were partitioned into training (n=200), validation (n=40), and testing (n=100) datasets. All participants performed training and validation with the corresponding datasets during the first (validation) phase of the Challenge. In the second (testing) phase, the participants used their model on the testing data to quantify the out‐of‐sample performance, which was hidden from participants and used to determine the final competition ranking. Participants also responded to a survey to summarize their models. Results: The Challenge attracted 195 participants from 28 countries, and 73 of those participants formed 44 teams in the validation phase, which received a total of 1750 submissions. The testing phase garnered submissions from 28 of those teams, which represents 28 unique prediction methods. On average, over the course of the validation phase, participants improved the dose and DVH scores of their models by a factor of 2.7 and 5.7, respectively. In the testing phase one model achieved the best dose score (2.429) and DVH score (1.478), which were both significantly better than the dose score (2.564) and the DVH score (1.529) that was achieved by the runner‐up models. Lastly, many of the top performing teams reported that they used generalizable techniques (e.g., ensembles) to achieve higher performance than their competition. Conclusion: OpenKBP is the first competition for knowledge‐based planning research. The Challenge helped launch the first platform that enables researchers to compare KBP prediction methods fairly and consistently using a large open‐source dataset and standardized metrics. OpenKBP has also democratized KBP research by making it accessible to everyone, which should help accelerate the progress of KBP research. The OpenKBP datasets are available publicly to help benchmark future KBP research. [ABSTRACT FROM AUTHOR]

It is known that a sequence {Πi}i∈N of permutations is quasirandom if and only if the pattern density of every 4‐point permutation in Πi converges to 1/24. We show that there is a set S of 4‐point permutations such that the sum of the pattern densities of the permutations from S in the permutations Πi converges to |S|/24 if and only if the sequence is quasirandom. Moreover, we are able to completely characterize the sets S with this property. In particular, there are exactly ten such sets, the smallest of which has cardinality eight. [ABSTRACT FROM AUTHOR]

Purpose: To develop a knowledge‐based automated planning pipeline that generates treatment plans without feature engineering, using deep neural network architectures for predicting three‐dimensional (3D) dose. Methods: Our knowledge‐based automated planning (KBAP) pipeline consisted of a knowledge‐based planning (KBP) method that predicts dose for a contoured computed tomography (CT) image followed by two optimization models that learn objective function weights and generate fluence‐based plans, respectively. We developed a novel generative adversarial network (GAN)‐based KBP approach, a 3D GAN model, which predicts dose for the full 3D CT image at once and accounts for correlations between adjacent CT slices. Baseline comparisons were made against two state‐of‐the‐art deep learning–based KBP methods from the literature. We also developed an additional benchmark, a two‐dimensional (2D) GAN model which predicts dose to each axial slice independently. For all models, we investigated the impact of multiplicatively scaling the predictions before optimization, such that the predicted dose distributions achieved all target clinical criteria. Each KBP model was trained on 130 previously delivered oropharyngeal treatment plans. Performance was tested on 87 out‐of‐sample previously delivered treatment plans. All KBAP plans were evaluated using clinical planning criteria and compared to their corresponding clinical plans. KBP prediction quality was assessed using dose‐volume histogram (DVH) differences from the corresponding clinical plans. Results: The best performing KBAP plans were generated using predictions from the 3D GAN model that were multiplicatively scaled. These plans satisfied 77% of all clinical criteria, compared to the clinical plans, which satisfied 67% of all criteria. In general, multiplicatively scaling predictions prior to optimization increased the fraction of clinical criteria satisfaction by 11% relative to the plans generated with nonscaled predictions. Additionally, these KBAP plans satisfied the same criteria as the clinical plans 84% and 8% more frequently as compared to the two benchmark methods, respectively. Conclusions: We developed the first knowledge‐based automated planning framework using a 3D generative adversarial network for prediction. Our results, based on 217 oropharyngeal cancer treatment plans, demonstrated superior performance in satisfying clinical criteria and generated more realistic plans as compared to the previous state‐of‐the‐art approaches. [ABSTRACT FROM AUTHOR]

One of the key challenges with big data is leveraging the complex network of information to yield useful clinical insights. The confluence of massive amounts of health data and a desire to make inferences and insights on these data has produced a substantial amount of interest in machine‐learning analytic methods. There has been a drastic increase in the otolaryngology literature volume describing novel applications of machine learning within the past 5 years. In this timely contemporary review, we provide an overview of popular machine‐learning techniques, and review recent machine‐learning applications in otolaryngology–head and neck surgery including neurotology, head and neck oncology, laryngology, and rhinology. Investigators have realized significant success in validated models with model sensitivities and specificities approaching 100%. Challenges remain in the implementation of machine‐learning algorithms. This may be in part the unfamiliarity of these techniques to clinician leaders on the front lines of patient care. Spreading awareness and confidence in machine learning will follow with further validation and proof‐of‐value analyses that demonstrate model performance superiority over established methods. We are poised to see a greater influx of machine‐learning applications to clinical problems in otolaryngology–head and neck surgery, and it is prudent for providers to understand the potential benefits and limitations of these technologies. Laryngoscope, 130:45–51, 2020 [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to automatically generate radiation therapy plans for oropharynx patients by combining knowledge‐based planning (KBP) predictions with an inverse optimization (IO) pipeline. Methods: We developed two KBP approaches, the bagging query (BQ) method and the generalized principal component analysis‐based (gPCA) method, to predict achievable dose–volume histograms (DVHs). These approaches generalize existing methods by predicting physically feasible organ‐at‐risk (OAR) and target DVHs in sites with multiple targets. Using leave‐one‐out cross validation, we applied both models to a large dataset of 217 oropharynx patients. The predicted DVHs were input into an IO pipeline that generated treatment plans (BQ and gPCA plans) via an intermediate step that estimated objective function weights for an inverse planning model. The KBP predictions were compared to the clinical DVHs for benchmarking. To assess the complete pipeline, we compared the BQ and gPCA plans to both the predictions and clinical plans. To isolate the effect of the KBP predictions, we put clinical DVHs through the IO pipeline to produce clinical inverse optimized (CIO) plans. This approach also allowed us to estimate the complexity of the clinical plans. The BQ and gPCA plans were benchmarked against the CIO plans using DVH differences and clinical planning criteria. Iso‐complexity plans (relative to CIO) were also generated and evaluated. Results: The BQ method tended to predict that less dose is delivered than what was observed in the clinical plans while the gPCA predictions were more similar to clinical DVHs. Both populations of KBP predictions were reproduced with inverse plans to within a median DVH difference of 3 Gy. Clinical planning criteria for OARs were satisfied most frequently by the BQ plans (74.4%), by 6.3% points more than the clinical plans. Meanwhile, target criteria were satisfied most frequently by the gPCA plans (90.2%), and by 21.2% points more than clinical plans. However, once the complexity of the plans was constrained to that of the CIO plans, the performance of the BQ plans degraded significantly. In contrast, the gPCA plans still satisfied more clinical criteria than both the clinical and CIO plans, with the most notable improvement being in target criteria. Conclusion: Our automated pipeline can successfully use DVH predictions to generate high‐quality plans without human intervention. Between the two KBP methods, gPCA plans tend to achieve comparable performance as clinical plans, even when controlling for plan complexity, whereas BQ plans tended to underperform. [ABSTRACT FROM AUTHOR]

Purpose: To determine how training set size affects the accuracy of knowledge-based treatment planning (KBP) models. Methods: The authors selected four models from three classes of KBP approaches, corresponding to three distinct quantities that KBP models may predict: dose-volume histogram (DVH) points, DVH curves, and objective function weights. DVH point prediction is done using the best plan from a database of similar clinical plans; DVH curve prediction employs principal component analysis and multiple linear regression; and objective function weights uses either logistic regression or K-nearest neighbors. The authors trained each KBP model using training sets of sizes n = 10, 20, 30, 50, 75, 100, 150, and 200. The authors set aside 100 randomly selected patients from their cohort of 315 prostate cancer patients from Princess Margaret Cancer Center to serve as a validation set for all experiments. For each value of n, the authors randomly selected 100 different training sets with replacement from the remaining 215 patients. Each of the 100 training sets was used to train a model for each value of n and for each KBT approach. To evaluate the models, the authors predicted the KBP endpoints for each of the 100 patients in the validation set. To estimate the minimum required sample size, the authors used statistical testing to determine if the median error for each sample size from 10 to 150 is equal to the median error for the maximum sample size of 200. Results: The minimum required sample size was different for each model. The DVH point prediction method predicts two dose metrics for the bladder and two for the rectum. The authors found that more than 200 samples were required to achieve consistent model predictions for all four metrics. For DVH curve prediction, the authors found that at least 75 samples were needed to accurately predict the bladder DVH, while only 20 samples were needed to predict the rectum DVH. Finally, for objective function weight prediction, at least 10 samples were needed to train the logistic regression model, while at least 150 samples were required to train the K-nearest neighbor methodology. Conclusions: In conclusion, the minimum required sample size needed to accurately train KBP models for prostate cancer depends on the specific model and endpoint to be predicted. The authors' results may provide a lower bound for more complicated tumor sites. [ABSTRACT FROM AUTHOR]

Adenoid cystic carcinoma ( AdCC) is a rare type of triple-negative breast cancer ( TNBC) characterized by the presence of the MYB-NFIB fusion gene. The molecular underpinning of breast AdCCs other than the MYB-NFIB fusion gene remains largely unexplored. Here we sought to define the repertoire of somatic genetic alterations of breast AdCCs. We performed whole-exome sequencing, followed by orthogonal validation, of 12 breast AdCCs to determine the landscape of somatic mutations and gene copy number alterations. Fluorescence in situ hybridization and reverse-transcription PCR were used to define the presence of MYB gene rearrangements and MYB-NFIB chimeric transcripts. Unlike common forms of TNBC, we found that AdCCs have a low mutation rate (0.27 non-silent mutations/Mb), lack mutations in TP53 and PIK3CA and display a heterogeneous constellation of known cancer genes affected by somatic mutations, including MYB, BRAF, FBXW7, SMARCA5, SF3B1 and FGFR2. MYB and TLN2 were affected by somatic mutations in two cases each. Akin to salivary gland AdCCs, breast AdCCs were found to harbour mutations targeting chromatin remodelling, cell adhesion, RNA biology, ubiquitination and canonical signalling pathway genes. We observed that, although breast AdCCs had rather simple genomes, they likely display intra-tumour genetic heterogeneity at diagnosis. Taken together, these findings demonstrate that the mutational burden and mutational repertoire of breast AdCCs are more similar to those of salivary gland AdCCs than to those of other types of TNBCs, emphasizing the importance of histological subtyping of TNBCs. Furthermore, our data provide direct evidence that AdCCs harbour a distinctive mutational landscape and genomic structure, irrespective of the disease site of origin. Copyright © 2015 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Purpose: There is evidence that computed tomography (CT) and positron emission tomography (PET) imaging metrics are prognostic and predictive in nonsmall cell lung cancer (NSCLC) treatment outcomes. However, few studies have explored the use of standardized uptake value (SUV)-based image features of nodal regions as predictive features. The authors investigated and compared the use of tumor and node image features extracted from the radiotherapy target volumes to predict relapse in a cohort of NSCLC patients undergoing chemoradiation treatment. Methods: A prospective cohort of 25 patients with locally advanced NSCLC underwent 4DPET/4DCT imaging for radiation planning. Thirty-seven image features were derived from the CT-defined volumes and SUVs of the PET image from both the tumor and nodal target regions. The machine learning methods of logistic regression and repeated stratified five-fold cross-validation (CV) were used to predict local and overall relapses in 2 yr. The authors used well-known feature selection methods (Spearman's rank correlation, recursive feature elimination) within each fold of CV. Classifiers were ranked on their Matthew's correlation coefficient (MCC) after CV. Area under the curve, sensitivity, and specificity values are also presented. Results: For predicting local relapse, the best classifier found had a mean MCC of 0.07 and was composed of eight tumor features. For predicting overall relapse, the best classifier found had a mean MCC of 0.29 and was composed of a single feature: the volume greater than 0.5 times the maximum SUV (N). Conclusions: The best classifier for predicting local relapse had only tumor features. In contrast, the best classifier for predicting overall relapse included a node feature. Overall, the methods showed that nodes add value in predicting overall relapse but not local relapse. [ABSTRACT FROM AUTHOR]

Aim The prognostic significance of KRAS, NRAS, PIK3CA and BRAF mutations was evaluated in Chinese patients with metastatic colorectal cancer (CRC). Method Tumor samples from 183 patients were retrospectively tested for KRAS, NRAS, PIK3CA and BRAF mutations. Multivariate analysis was performed to determine the relationship between mutational status, drug response and survival. Result Over 70% of patients received two or more lines of chemotherapy, 50% had cetuximab and 18% had bevacizumab. The prevalence of KRAS, NRAS, BRAF and PIK3CA mutations was 45, 3.2, 5 and 20%, respectively. For the entire cohort, the median overall survival was 24 months (95% confidence interval [ CI] = 20.4-26.4 months). Of the genes tested, only KRAS mutation was an independent prognostic factor with a multivariate hazard ratio of 1.5 (95% CI = 1.05-2.16, P = 0.03). In the subgroup of patients who received cetuximab-based therapy in the first-line setting, KRAS mutation was associated with a lack of response to chemotherapy (28% vs 66%, chi-square, P = 0.01). Patients with KRAS mutant tumors (or KRAS wild-type tumors that harbored BRAF and/or PIK3CA mutations) tended to have lower response rates to chemotherapy and/or cetuximab ( P = not significant). The number of NRAS mutant cases was too small to allow any statistical analysis. Conclusion The prevalence of KRAS, NRAS, BRAF and PIK3CA mutations in this cohort is consistent with reports from non-Asian populations, and KRAS mutation has both prognostic and predictive significance in Chinese patients with metastatic CRC. [ABSTRACT FROM AUTHOR]

Carcinogenesis is a multistep process attributable to both gain-of-function mutations in oncogenes and loss-of-function mutations in tumor suppressor genes. Currently, most molecular targeted therapies are inhibitors of oncogenes, because inactivated tumor suppressor genes have proven harder to 'drug.' Nevertheless, in cancers, tumor suppressor genes undergo alteration more frequently than do oncogenes. In recent years, several promising strategies directed at tumor suppressor genes, or the pathways controlled by these genes, have emerged. Here, we describe advances in a number of different methodologies aimed at therapeutically targeting tumors driven by inactivated tumor suppressor genes. Cancer 2015;121:1357-1368. © 2014 American Cancer Society. [ABSTRACT FROM AUTHOR]

Purpose: In left-sided tangential breast intensity modulated radiation therapy (IMRT), the heart may enter the radiation field and receive excessive radiation while the patient is breathing. The patient's breathing pattern is often irregular and unpredictable. We verify the clinical applicability of a heart-sparing robust optimization approach for breast IMRT. We compare robust optimized plans with clinical plans at free-breathing and clinical plans at deep inspiration breath-hold (DIBH) using active breathing control (ABC). Methods: Eight patients were included in the study with each patient simulated using 4D-CT. The 4D-CT image acquisition generated ten breathing phase datasets. An average scan was constructed using all the phase datasets. Two of the eight patients were also imaged at breath-hold using ABC. The 4D-CT datasets were used to calculate the accumulated dose for robust optimized and clinical plans based on deformable registration. We generated a set of simulated breathing probability mass functions, which represent the fraction of time patients spend in different breathing phases. The robust optimization method was applied to each patient using a set of dose-influence matrices extracted from the 4D-CT data and a model of the breathing motion uncertainty. The goal of the optimization models was to minimize the dose to the heart while ensuring dose constraints on the target were achieved under breathing motion uncertainty. Results: Robust optimized plans were improved or equivalent to the clinical plans in terms of heart sparing for all patients studied. The robust method reduced the accumulated heart dose (D10cc) by up to 801 cGy compared to the clinical method while also improving the coverage of the accumulated whole breast target volume. On average, the robust method reduced the heart dose (D10cc) by 364 cGy and improved the optBreast dose (D99%) by 477 cGy. In addition, the robust method had smaller deviations from the planned dose to the accumulated dose. The deviation of the accumulated dose from the planned dose for the optBreast (D99%) was 12 cGy for robust versus 445 cGy for clinical. The deviation for the heart (D10cc) was 41 cGy for robust and 320 cGy for clinical. Conclusions: The robust optimization approach can reduce heart dose compared to the clinical method at free-breathing and can potentially reduce the need for breath-hold techniques. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. Methods: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and applied three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. Results: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. Conclusions: The authors demonstrated that the KNN and MLR weight prediction methodologies perform comparably to the LR model and can produce clinical quality treatment plans by simultaneously predicting multiple weights that capture trade-offs associated with sparing multiple OARs. [ABSTRACT FROM AUTHOR]

Amyloid beta ( Aβ), a key component in the pathophysiology of Alzheimer's disease, is thought to target excitatory synapses early in the disease. However, the mechanism by which Aβ weakens synapses is not well understood. Here we showed that the PDZ domain protein, protein interacting with C kinase 1 ( PICK1), was required for Aβ to weaken synapses. In mice lacking PICK1, elevations of Aβ failed to depress synaptic transmission in cultured brain slices. In dissociated cultured neurons, Aβ failed to reduce surface α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor subunit 2, a subunit of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors that binds with PICK1 through a PDZ ligand-domain interaction. Lastly, a novel small molecule ( BIO922) discovered through structure-based drug design that targets the specific interactions between Glu A2 and PICK1 blocked the effects of Aβ on synapses and surface receptors. We concluded that Glu A2- PICK1 interactions are a key component of the effects of Aβ on synapses. [ABSTRACT FROM AUTHOR]

Purpose: To develop mathematical models to predict the evolution of tumor geometry in cervical cancer undergoing radiation therapy. Methods: The authors develop two mathematical models to estimate tumor geometry change: a Markov model and an isomorphic shrinkage model. The Markov model describes tumor evolution by investigating the change in state (either tumor or nontumor) of voxels on the tumor surface. It assumes that the evolution follows a Markov process. Transition probabilities are obtained using maximum likelihood estimation and depend on the states of neighboring voxels. The isomorphic shrinkage model describes tumor shrinkage or growth in terms of layers of voxels on the tumor surface, instead of modeling individual voxels. The two proposed models were applied to data from 29 cervical cancer patients treated at Princess Margaret Cancer Centre and then compared to a constant volume approach. Model performance was measured using sensitivity and specificity. Results: The Markov model outperformed both the isomorphic shrinkage and constant volume models in terms of the trade-off between sensitivity (target coverage) and specificity (normal tissue sparing). Generally, the Markov model achieved a few percentage points in improvement in either sensitivity or specificity compared to the other models. The isomorphic shrinkage model was comparable to the Markov approach under certain parameter settings. Convex tumor shapes were easier to predict. Conclusions: By modeling tumor geometry change at the voxel level using a probabilistic model, improvements in target coverage and normal tissue sparing are possible. Our Markov model is flexible and has tunable parameters to adjust model performance to meet a range of criteria. Such a model may support the development of an adaptive paradigm for radiation therapy of cervical cancer. [ABSTRACT FROM AUTHOR]

Objectives. This study examined the efficacy of a group intervention, the Integrative Body-Mind-Spirit (I-BMS) intervention, which aims at improving the psychosocial and spiritual well-being of Chinese women undergoing their first IVF treatment cycle. Design. The I-BMS intervention facilitates the search of meaning of life in the context of family and childbearing, as well as the letting go of high IVF expectations. A randomized controlled study of 339 women undergoing first IVF treatment cycle in a local Hong Kong hospital was conducted (intervention: n= 172; no-intervention control: n= 167). Methods. Assessments of anxiety, perceived importance of childbearing, and spiritual well-being were made at randomization (T0), on the day starting ovarian stimulations (T1), and on the day undertaking embryo transfer (T2). Results. Comparing T0 and T2, interaction analyses showed women who had received the intervention reported lower levels of physical distress, anxiety, and disorientation. They reported being more tranquil and satisfied with their marriage, and saw childbearing as less important compared to women in the control group. Conclusions. These findings suggest that I-BMS intervention was successful at improving the psychosocial and spiritual well-being of women undergoing their first IVF treatment cycle. This study highlights the importance of providing integrative fertility treatment that incorporates psychosocial and spiritual dimensions. [ABSTRACT FROM AUTHOR]

Abstract: Background: The Memory Performance Index (MPI) quantifies the pattern of recalled and nonrecalled words of the Consortium to Establish a Registry for Alzheimer''s Disease Wordlist (CWL) onto a 0 to 100 scale and distinguishes normal from mild cognitive impairment with 96% to 97% accuracy. Methods: In group A, 121,481 independently living individuals, 18 to 106 years old, were assessed with the CWL and classified as cognitively impaired (N = 5,971) or normal (N = 115,510). The MPI and CWL immediate free recall (IFR), delayed free recall (DFR), and total free recall (TFR) scores (the outcome measures) were each regressed against predictors of age, gender, race, education, test administration method (in-person or telephone), and wordlist used. Predictor effect sizes (Cohen''s f2) were computed for each outcome. In addition, CWL plus Functional Assessment Staging Tests (FAST) were administered to 441 normal to moderately severely demented (FAST stages 1 to 6) patients (group B). Median MPI scores were tested for significant differences across FAST stage. Results: For group A, the variance explained by all predictors combined was MPI = 55.0%, IFR = 24.9%, DFR = 23.4%, and TFR = 26.9%. The age effect size on MPI score was large, but it was small on IFR, DFR, and TFR. The other predictors all had negligible (<0.02) or small effect sizes (0.02 to 0.15). For group B, median MPI scores progressively declined across all FAST stages (P < .0002). Conclusions: MPI score progressively declines with increasing dementia severity. Also, MPI score explains 2 to 3 times more variance than total scores, which improves ability to detect treatment effects. [Copyright &y& Elsevier]

Internal organ motion during the course of radiation therapy of cancer affects the distribution of the delivered dose and, generally, reduces its conformality to the targeted volume. Previously proposed approaches aimed at mitigating the effect of internal motion in intensity-modulated radiation therapy (IMRT) included expansion of the target margins, motion-correlated delivery (e.g., respiratory gating, tumor tracking), and adaptive treatment plan optimization employing a probabilistic description of motion. We describe and test the tumor trailing strategy, which utilizes the synergy of motion-adaptive treatment planning and delivery methods. We regard the (rigid) target motion as a superposition of a relatively fast cyclic component (e.g., respiratory) and slow aperiodic trends (e.g., the drift of exhalation baseline). In the trailing approach, these two components of motion are decoupled and dealt with separately. Real-time motion monitoring is employed to identify the “slow” shifts, which are then corrected by applying setup adjustments. The delivery does not track the target position exactly, but trails the systematic trend due to the delay between the time a shift occurs, is reliably detected, and, subsequently, corrected. The “fast” cyclic motion is accounted for with a robust motion-adaptive treatment planning, which allows for variability in motion parameters (e.g., mean and extrema of the tidal volume, variable period of respiration, and expiratory duration). Motion-surrogate data from gated IMRT treatments were used to provide probability distribution data for motion-adaptive planning and to test algorithms that identified systematic trends in the character of motion. Sample IMRT fields were delivered on a clinical linear accelerator to a programmable moving phantom. Dose measurements were performed with a commercial two-dimensional ion-chamber array. The results indicate that by reducing intrafractional motion variability, the trailing strategy enhances relevance and applicability of motion-adaptive planning methods, and improves conformality of the delivered dose to the target in the presence of irregular motion. Trailing strategy can be applied to respiratory-gated treatments, in which the correction for the slow motion can increase the duty cycle, while robust probabilistic planning can improve management of the residual motion within the gate window. Similarly, trailing may improve the dose conformality in treatment of patients who exhibit detectable target motion of low amplitude, which is considered insufficient to provide a clinical indication for the use of respiratory-gated treatment (e.g., peak-to-peak motion of less than 10 mm). The mechanical limitations of implementing tumor trailing are less rigorous than those of real-time tracking, and the same technology could be used for both. [ABSTRACT FROM AUTHOR]

Psychosocial programs designed for cancer patients often facilitate the search for meaning as one of the therapeutic components. This study aimed to develop a self-report instrument, namely Chinese Cancer Coherence Scale (CCCS), which measures the patients' meaning-making process with reference to the concept of coherence. A panel of eight veteran social workers and psychologists generated statements pertaining to the cancer experience. Results from a two-phase study involving 390 breast cancer patients revealed a two-factor structure of the CCCS, namely incoherent-embittered and coherent-enlightened. The use of the CCCS by practitioners and researchers is recommended in order to understand how Chinese cancer patients make sense of their cancer experience. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

One of the key challenges with big data is leveraging the complex network of information to yield useful clinical insights. The confluence of massive amounts of health data and a desire to make inferences and insights on these data has produced a substantial amount of interest in machine-learning analytic methods. There has been a drastic increase in the otolaryngology literature volume describing novel applications of machine learning within the past 5 years. In this timely contemporary review, we provide an overview of popular machine-learning techniques, and review recent machine-learning applications in otolaryngology-head and neck surgery including neurotology, head and neck oncology, laryngology, and rhinology. Investigators have realized significant success in validated models with model sensitivities and specificities approaching 100%. Challenges remain in the implementation of machine-learning algorithms. This may be in part the unfamiliarity of these techniques to clinician leaders on the front lines of patient care. Spreading awareness and confidence in machine learning will follow with further validation and proof-of-value analyses that demonstrate model performance superiority over established methods. We are poised to see a greater influx of machine-learning applications to clinical problems in otolaryngology-head and neck surgery, and it is prudent for providers to understand the potential benefits and limitations of these technologies. Laryngoscope, 130:45-51, 2020. [ABSTRACT FROM AUTHOR]

Purpose: Intensity-modulated radiation therapy (IMRT) treatment planning typically combines multiple criteria into a single objective function by taking a weighted sum. The authors propose a statistical model that predicts objective function weights from patient anatomy for prostate IMRT treatment planning. This study provides a proof of concept for geometry-driven weight determination. Methods: A previously developed inverse optimization method (IOM) was used to generate optimal objective function weights for 24 patients using their historical treatment plans (i.e., dose distributions). These IOM weights were around 1% for each of the femoral heads, while bladder and rectum weights varied greatly between patients. A regression model was developed to predict a patient's rectum weight using the ratio of the overlap volume of the rectum and bladder with the planning target volume at a 1 cm expansion as the independent variable. The femoral head weights were fixed to 1% each and the bladder weight was calculated as one minus the rectum and femoral head weights. The model was validated using leave-one-out cross validation. Objective values and dose distributions generated through inverse planning using the predicted weights were compared to those generated using the original IOM weights, as well as an average of the IOM weights across all patients. Results: The IOM weight vectors were on average six times closer to the predicted weight vectors than to the average weight vector, using l2 distance. Likewise, the bladder and rectum objective values achieved by the predicted weights were more similar to the objective values achieved by the IOM weights. The difference in objective value performance between the predicted and average weights was statistically significant according to a one-sided sign test. For all patients, the difference in rectum V54.3 Gy, rectum V70.0 Gy, bladder V54.3 Gy, and bladder V70.0 Gy values between the dose distributions generated by the predicted weights and IOM weights was less than 5 percentage points. Similarly, the difference in femoral head V54.3 Gy values between the two dose distributions was less than 5 percentage points for all but one patient. Conclusions: This study demonstrates a proof of concept that patient anatomy can be used to predict appropriate objective function weights for treatment planning. In the long term, such geometry-driven weights may serve as a starting point for iterative treatment plan design or may provide information about the most clinically relevant region of the Pareto surface to explore. [ABSTRACT FROM AUTHOR]