Online heat transfer measurement and analysis for sugar mill evaporators

Fouling and scaling in evaporators has been an area of great interest to raw sugar mills for a number of years and many of the mechanisms causing the scale and the rates of scaling are unknown. In an attempt to quantify the scaling rates and measure the scaling, an online model has been developed to model a system of evaporators. Monitoring the heat transfer coefficient as a function of time enabled measurement of the scaling rate by monitoring the heat transfer coefficient as it decreased with time. It is assumed that the scaling on the juice side of the evaporators with time is the only contributor to the drop in heat transfer and that other effects such as the fouling of the steam side are negligible. A large problem in the past was reliable measurements of the heat transfer coefficient on evaporators. This was usually because the procedure required multiple measurements that needed to be taken by expensive equipment, which meant large amounts of capital to set up the monitoring systems. Another issue in the past was the iterative nature of the calculations, but with the advancements in computer processing and control systems, it is now possible to setup a control system that requires fewer inputs and low-cost instrumentation to get accurate measurements of the heat transfer coefficient. Utilizing the processing power of the control computer to perform multiple iterations allows for quick convergence to the solution of the heat transfer coefficient for the system. The scaling rates are measured on a simple quadruple effect evaporator with no vapor bleeds at the St James sugar mill in Louisiana. A model was developed to predict the scaling rates of the individual effects based on the heat transfer coefficient measurements. Analysis of the juice and syrup streams to and from the evaporators was performed to quantify the scale components in the scale that accumulates in the evaporators. The measurement of the heat transfer coefficient shows, as is sometimes seen in practice, that the final effect is the effect that will scale the most and determines the need for the effect to come offline to be cleaned. This information on the degree of scaling is useful for the mills as they can use this to optimize cleaning of the effects. The measured values of the heat transfer coefficients fell within measured values from other sugar milling countries. The results show that the use of computers enables one to calculate a heat transfer coefficient for a system of evaporators in real time

Biological Effects of Antiprotons Are Antiprotons a Candidate for Cancer Therapy?

2009 Status Report of AD-4 Experimen

NRG Oncology Survey on Practice and Technology Use in SRT and SBRT Delivery

PurposeTo assess stereotactic radiotherapy (SRT)/stereotactic body radiotherapy (SBRT) practices by polling clinics participating in multi-institutional clinical trials.MethodsThe NRG Oncology Medical Physics Subcommittee distributed a survey consisting of 23 questions, which covered general technologies, policies, and procedures used in the Radiation Oncology field for the delivery of SRT/SBRT (9 questions), and site-specific questions for brain SRT, lung SBRT, and prostate SBRT (14 questions). Surveys were distributed to 1,996 radiotherapy institutions included on the membership rosters of the five National Clinical Trials Network (NCTN) groups. Patient setup, motion management, target localization, prescriptions, and treatment delivery technique data were reported back by 568 institutions (28%).Results97.5% of respondents treat lung SBRT patients, 77.0% perform brain SRT, and 29.1% deliver prostate SBRT. 48.8% of clinics require a physicist present for every fraction of SBRT, 18.5% require a physicist present for the initial SBRT fraction only, and 14.9% require a physicist present for the entire first fraction, including set-up approval for all subsequent fractions. 55.3% require physician approval for all fractions, and 86.7% do not reposition without x-ray imaging. For brain SRT, most institutions (83.9%) use a planning target volume (PTV) margin of 2 mm or less. Lung SBRT PTV margins of 3 mm or more are used in 80.6% of clinics. Volumetric modulated arc therapy (VMAT) is the dominant delivery method in 62.8% of SRT treatments, 70.9% of lung SBRT, and 68.3% of prostate SBRT.ConclusionThis report characterizes SRT/SBRT practices in radiotherapy clinics participating in clinical trials. Data made available here allows the radiotherapy community to compare their practice with that of other clinics, determine what is achievable, and assess areas for improvement

Preoperative and postoperative prediction of long-term meningioma outcomes.

BackgroundMeningiomas are stratified according to tumor grade and extent of resection, often in isolation of other clinical variables. Here, we use machine learning (ML) to integrate demographic, clinical, radiographic and pathologic data to develop predictive models for meningioma outcomes.Methods and findingsWe developed a comprehensive database containing information from 235 patients who underwent surgery for 257 meningiomas at a single institution from 1990 to 2015. The median follow-up was 4.3 years, and resection specimens were re-evaluated according to current diagnostic criteria, revealing 128 WHO grade I, 104 grade II and 25 grade III meningiomas. A series of ML algorithms were trained and tuned by nested resampling to create models based on preoperative features, conventional postoperative features, or both. We compared different algorithms' accuracy as well as the unique insights they offered into the data. Machine learning models restricted to preoperative information, such as patient demographics and radiographic features, had similar accuracy for predicting local failure (AUC = 0.74) or overall survival (AUC = 0.68) as models based on meningioma grade and extent of resection (AUC = 0.73 and AUC = 0.72, respectively). Integrated models incorporating all available demographic, clinical, radiographic and pathologic data provided the most accurate estimates (AUC = 0.78 and AUC = 0.74, respectively). From these models, we developed decision trees and nomograms to estimate the risks of local failure or overall survival for meningioma patients.ConclusionsClinical information has been historically underutilized in the prediction of meningioma outcomes. Predictive models trained on preoperative clinical data perform comparably to conventional models trained on meningioma grade and extent of resection. Combination of all available information can help stratify meningioma patients more accurately

Photons, protons or carbon ions for stage I non-small cell lung cancer - Results of the multicentric ROCOCO in silico study

Purpose: To compare dose to organs at risk (OARs) and dose-escalation possibility for 24 stage I non-small cell lung cancer (NSCLC) patients in a ROCOCO (Radiation Oncology Collaborative Comparison) trial.Methods: For each patient, 3 photon plans [Intensity-modulated radiotherapy (IMRT), volumetric modulated arc therapy (VMAT) and CyberKnife], a double scattered proton (DSP) and an intensity-modulated carbon-ion (IMIT) therapy plan were created. Dose prescription was 60 Gy (equivalent) in 8 fractions.Results: The mean dose and dose to 2% of the clinical target volume (CTV) were lower for protons and ions compared with IMRT (p < 0.01). Doses to the lungs, heart, and mediastinal structures were lowest with IMIT (p < 0.01), doses to the spinal cord were lowest with DSP (p < 0.01). VMAT and CyberKnife allowed for reduced doses to most OARs compared with IMRT. Dose escalation was possible for 8 patients. Generally, the mediastinum was the primary dose-limiting organ.Conclusion: On average, the doses to the OARs were lowest using particles, with more homogenous CTV doses. Given the ability of VMAT and CyberKnife to limit doses to OARs compared with IMRT, the additional benefit of particles may only be clinically relevant in selected patients and thus should be carefully weighed for every individual patient. (C) 2018 Elsevier B.V. All rights reserved.Biological, physical and clinical aspects of cancer treatment with ionising radiatio

Photons or protons for reirradiation in (non-)small cell lung cancer:Results of the multicentric ROCOCO in silico study

OBJECTIVE: Locally recurrent disease is of increasing concern in (non-)small cell lung cancer [(N)SCLC] patients. Local reirradiation with photons or particles may be of benefit to these patients. In this multicentre in silico trial performed within the Radiation Oncology Collaborative Comparison (ROCOCO) consortium, the doses to the target volumes and organs at risk (OARs) were compared when using several photon and proton techniques in patients with recurrent localised lung cancer scheduled to undergo reirradiation. METHODS: 24 consecutive patients with a second primary (N)SCLC or recurrent disease after curative-intent, standard fractionated radio(chemo)therapy were included in this study. The target volumes and OARs were centrally contoured and distributed to the participating ROCOCO sites. Remaining doses to the OARs were calculated on an individual patient's basis. Treatment planning was performed by the participating site using the clinical treatment planning system and associated beam characteristics. RESULTS: Treatment plans for all modalities (five photon and two proton plans per patient) were available for 22 patients (N = 154 plans). 3D-conformal photon therapy and double-scattered proton therapy delivered significantly lower doses to the target volumes. The highly conformal techniques, i.e., intensity modulated radiation therapy (IMRT), volumetric modulated arc therapy (VMAT), CyberKnife, TomoTherapy and intensity-modulated proton therapy (IMPT), reached the highest doses in the target volumes. Of these, IMPT was able to statistically significantly decrease the radiation doses to the OARs. CONCLUSION: Highly conformal photon and proton beam techniques enable high-dose reirradiation of the target volume. They, however, significantly differ in the dose deposited in the OARs. The therapeutic options, i.e., reirradiation or systemic therapy, need to be carefully weighed and discussed with the patients. ADVANCES IN KNOWLEDGE: Highly conformal photon and proton beam techniques enable high-dose reirradiation of the target volume. In light of the abilities of the various highly conformal techniques to spare specific OARs, the therapeutic options need to be carefully weighed and patients included in the decision-making process

Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers.

PurposeMachine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector machine, gradient boosting) in terms of classification performance. The purpose of this study is to compare such classifiers specifically for (chemo)radiotherapy datasets and to estimate their average discriminative performance for radiation treatment outcome prediction.MethodsWe collected 12 datasets (3496 patients) from prior studies on post-(chemo)radiotherapy toxicity, survival, or tumor control with clinical, dosimetric, or blood biomarker features from multiple institutions and for different tumor sites, that is, (non-)small-cell lung cancer, head and neck cancer, and meningioma. Six common classification algorithms with built-in feature selection (decision tree, random forest, neural network, support vector machine, elastic net logistic regression, LogitBoost) were applied on each dataset using the popular open-source R package caret. The R code and documentation for the analysis are available online (https://github.com/timodeist/classifier_selection_code). All classifiers were run on each dataset in a 100-repeated nested fivefold cross-validation with hyperparameter tuning. Performance metrics (AUC, calibration slope and intercept, accuracy, Cohen's kappa, and Brier score) were computed. We ranked classifiers by AUC to determine which classifier is likely to also perform well in future studies. We simulated the benefit for potential investigators to select a certain classifier for a new dataset based on our study (pre-selection based on other datasets) or estimating the best classifier for a dataset (set-specific selection based on information from the new dataset) compared with uninformed classifier selection (random selection).ResultsRandom forest (best in 6/12 datasets) and elastic net logistic regression (best in 4/12 datasets) showed the overall best discrimination, but there was no single best classifier across datasets. Both classifiers had a median AUC rank of 2. Preselection and set-specific selection yielded a significant average AUC improvement of 0.02 and 0.02 over random selection with an average AUC rank improvement of 0.42 and 0.66, respectively.ConclusionRandom forest and elastic net logistic regression yield higher discriminative performance in (chemo)radiotherapy outcome and toxicity prediction than other studied classifiers. Thus, one of these two classifiers should be the first choice for investigators when building classification models or to benchmark one's own modeling results against. Our results also show that an informed preselection of classifiers based on existing datasets can improve discrimination over random selection