Intensity Modulated Therapy for Patients With Breast Cancer. Practical Guidelines and Tips for an Effective Treatment Planning Strategy.

Purpose: Practical guidelines and tips for effective and robust radiation therapy treatment planning for patients with breast cancer are addressed for fixed-field intensity modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT) techniques. The concepts described here are general and valid on all treatment planning systems. However, some details shown here have been applied to the Varian platforms used at the authors' institutions. Methods and Materials: The specific aspects of using C-arm- or O-ring-mounted linear accelerators are covered in the document, as well as tips for dealing with certain resource constraints, target cropping, and skin flash aiming to reduce risks of skin toxicity and to manage (residual after breath control) respiration motion or edema. Results: A decision tree is presented, and practical solutions for cases where a target volume is contoured or not and where volumetric modulated arc therapy or fixed-beam intensity modulation should be applied and details about the technical implementation (tangential IMRT, butterfly IMRT or VMAT, and large partial VMAT arcs) are discussed. Target cropping and skin flash implications are discussed in detail, and links to plan robustness are outlined. Conclusions: Practical guidelines for breast planning are presented and summarized with a decision tree and technical summaries.

Fumonisin B1 protects against long-chained polyunsaturated fatty acid-induced cell death in HepG2 cells - implications for cancer promotion.

Fumonisin B1 (FB1), a food-borne mycotoxin, is a cancer promoter in rodent liver and augments proliferation of initiated cells while inhibiting the growth of normal hepatocytes by disrupting lipid biosynthesis at various levels. HepG2 cancer cells exhibited resistance to FB1-induced toxic effects presumably due to their low content of polyunsaturated fatty acids (PUFA) even though FB1-typical lipid changes were observed, e.g. significantly increased phosphatidylethanolamine (PE), decreased sphingomyelin and cholesterol content, increased sphinganine (Sa) and sphinganine/sphingosine ratio, increased C18:1ω-9, decreased C20:4ω-6 content in PE and decreased C20:4ω-6_PC/PE ratio. Increasing PUFA content of HepG2 cells with phosphatidylcholine (PC) vesicles containing C20:4ω-6 (SAPC) or C22:6ω-3 (SDPC) disrupted cell survival, cellular redox status and induced oxidative stress and apoptosis. A partially protective effect of FB1 was evident in PUFA-enriched HepG2 cells which may be related to the FB1-induced reduction in oxidative stress and the disruption of key cell membrane constituents indicative of a resistant lipid phenotype. Interactions between different ω-6 and ω-3 PUFA, membrane constituents including cholesterol, and the glycerophospho- and sphingolipids and FB1 in this cell model provide further support for the resistant lipid phenotype and its role in the complex cellular effects underlying the cancer promoting potential of the fumonisins.

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources.

Access to radiotherapy worldwide is limited. The Radiation Planning Assistant (RPA) is a fully automated, web-based tool that is being developed to offer fully automated radiotherapy treatment planning tools to clinics with limited resources. The goal is to help clinical teams scale their efforts, thus reaching more patients with cancer. The user connects to the RPA via a webpage, completes a Service Request (prescription and information about the radiotherapy targets), and uploads the patient's CT image set. The RPA offers two approaches to automated planning. In one-step planning, the system uses the Service Request and CT scan to automatically generate the necessary contours and treatment plan. In two-step planning, the user reviews and edits the automatically generated contours before the RPA continues to generate a volume-modulated arc therapy plan. The final plan is downloaded from the RPA website and imported into the user's local treatment planning system, where the dose is recalculated for the locally commissioned linac; if necessary, the plan is edited prior to approval for clinical use.

Validation of an automated contouring and treatment planning tool for pediatric craniospinal radiation therapy.

Purpose: Treatment planning for craniospinal irradiation (CSI) is complex and time-consuming, especially for resource-constrained centers. To alleviate demanding workflows, we successfully automated the pediatric CSI planning pipeline in previous work. In this work, we validated our CSI autosegmentation and autoplanning tool on a large dataset from St. Jude Children's Research Hospital. Methods: Sixty-three CSI patient CT scans were involved in the study. Pre-planning scripts were used to automatically verify anatomical compatibility with the autoplanning tool. The autoplanning pipeline generated 15 contours and a composite CSI treatment plan for each of the compatible test patients (n=51). Plan quality was evaluated quantitatively with target coverage and dose to normal tissue metrics and qualitatively with physician review, using a 5-point Likert scale. Three pediatric radiation oncologists from 3 institutions reviewed and scored 15 contours and a corresponding composite CSI plan for the final 51 test patients. One patient was scored by 3 physicians, resulting in 53 plans scored total. Results: The algorithm automatically detected 12 incompatible patients due to insufficient junction spacing or head tilt and removed them from the study. Of the 795 autosegmented contours reviewed, 97% were scored as clinically acceptable, with 92% requiring no edits. Of the 53 plans scored, all 51 brain dose distributions were scored as clinically acceptable. For the spine dose distributions, 92%, 100%, and 68% of single, extended, and multiple-field cases, respectively, were scored as clinically acceptable. In all cases (major or minor edits), the physicians noted that they would rather edit the autoplan than create a new plan. Conclusions: We successfully validated an autoplanning pipeline on 51 patients from another institution, indicating that our algorithm is robust in its adjustment to differing patient populations. We automatically generated 15 contours and a comprehensive CSI treatment plan for each patient without physician intervention, indicating the potential for increased treatment planning efficiency and global access to high-quality radiation therapy.

Addressing the Global Expertise Gap in Radiation Oncology: The Radiation Planning Assistant.

PURPOSE: Automation, including the use of artificial intelligence, has been identified as a possible opportunity to help reduce the gap in access and quality for radiotherapy and other aspects of cancer care. The Radiation Planning Assistant (RPA) project was conceived in 2015 (and funded in 2016) to use automated contouring and treatment planning algorithms to support the efforts of oncologists in low- and middle-income countries, allowing them to scale their efforts and treat more patients safely and efficiently (to increase access). DESIGN: In this review, we discuss the development of the RPA, with a particular focus on clinical acceptability and safety/risk across jurisdictions as these are important indicators for the successful future deployment of the RPA to increase radiotherapy availability and ameliorate global disparities in access to radiation oncology. RESULTS: RPA tools will be offered through a webpage, where users can upload computed tomography data sets and download automatically generated contours and treatment plans. All interfaces have been designed to maximize ease of use and minimize risk. The current version of the RPA includes automated contouring and planning for head and neck cancer, cervical cancer, breast cancer, and metastases to the brain. CONCLUSION: The RPA has been designed to bring high-quality treatment planning to more patients across the world, and it may encourage greater investment in treatment devices and other aspects of cancer treatment.

Obesity promotes fumonisin B1 hepatotoxicity.

Obesity, which is a worldwide public health issue, is associated with chronic inflammation that contribute to long-term complications, including insulin resistance, type 2 diabetes and non-alcoholic fatty liver disease. We hypothesized that obesity may also influence the sensitivity to food contaminants, such as fumonisin B1 (FB1), a mycotoxin produced mainly by the Fusarium verticillioides. FB1, a common contaminant of corn, is the most abundant and best characterized member of the fumonisins family. We investigated whether diet-induced obesity could modulate the sensitivity to oral FB1 exposure, with emphasis on gut health and hepatotoxicity. Thus, metabolic effects of FB1 were assessed in obese and non-obese male C57BL/6J mice. Mice received a high-fat diet (HFD) or normal chow diet (CHOW) for 15 weeks. Then, during the last three weeks, mice were exposed to these diets in combination or not with FB1 (10 mg/kg body weight/day) through drinking water. As expected, HFD feeding induced significant body weight gain, increased fasting glycemia, and hepatic steatosis. Combined exposure to HFD and FB1 resulted in body weight loss and a decrease in fasting blood glucose level. This co-exposition also induces gut dysbiosis, an increase in plasma FB1 level, a decrease in liver weight and hepatic steatosis. Moreover, plasma transaminase levels were significantly increased and associated with liver inflammation in HFD/FB1-treated mice. Liver gene expression analysis revealed that the combined exposure to HFD and FB1 was associated with reduced expression of genes involved in lipogenesis and increased expression of immune response and cell cycle-associated genes. These results suggest that, in the context of obesity, FB1 exposure promotes gut dysbiosis and severe liver inflammation. To our knowledge, this study provides the first example of obesity-induced hepatitis in response to a food contaminant.

Resection cavity auto-contouring for patients with pediatric medulloblastoma using only CT information.

PURPOSE: Target delineation for radiation therapy is a time-consuming and complex task. Autocontouring gross tumor volumes (GTVs) has been shown to increase efficiency. However, there is limited literature on post-operative target delineation, particularly for CT-based studies. To this end, we trained a CT-based autocontouring model to contour the post-operative GTV of pediatric patients with medulloblastoma. METHODS: One hundred four retrospective pediatric CT scans were used to train a GTV auto-contouring model. Eighty patients were then preselected for contour visibility, continuity, and location to train an additional model. Each GTV was manually annotated with a visibility score based on the number of slices with a visible GTV (1 = < 25%, 2 = 25-50%, 3 = > 50-75%, and 4 = > 75-100%). Contrast and the contrast-to-noise ratio (CNR) were calculated for the GTV contour with respect to a cropped background image. Both models were tested on the original and pre-selected testing sets. The resulting surface and overlap metrics were calculated comparing the clinical and autocontoured GTVs and the corresponding clinical target volumes (CTVs). RESULTS: Eighty patients were pre-selected to have a continuous GTV within the posterior fossa. Of these, 7, 41, 21, and 11 were visibly scored as 4, 3, 2, and 1, respectively. The contrast and CNR removed an additional 11 and 20 patients from the dataset, respectively. The Dice similarity coefficients (DSC) were 0.61 ± 0.29 and 0.67 ± 0.22 on the models without pre-selected training data and 0.55 ± 13.01 and 0.83 ± 0.17 on the models with pre-selected data, respectively. The DSC on the CTV expansions were 0.90 ± 0.13. CONCLUSION: We successfully automatically contoured continuous GTVs within the posterior fossa on scans that had contrast > ± 10 HU. CT-Based auto-contouring algorithms have potential to positively impact centers with limited MRI access.

Automating the treatment planning process for 3D-conformal pediatric craniospinal irradiation therapy.

PURPOSE: Pediatric patients with medulloblastoma in low- and middle-income countries (LMICs) are most treated with 3D-conformal photon craniospinal irradiation (CSI), a time-consuming, complex treatment to plan, especially in resource-constrained settings. Therefore, we developed and tested a 3D-conformal CSI autoplanning tool for varying patient lengths. METHODS AND MATERIALS: Autocontours were generated with a deep learning model trained:tested (80:20 ratio) on 143 pediatric medulloblastoma CT scans (patient ages: 2-19 years, median = 7 years). Using the verified autocontours, the autoplanning tool generated two lateral brain fields matched to a single spine field, an extended single spine field, or two matched spine fields. Additional spine subfields were added to optimize the corresponding dose distribution. Feathering was implemented (yielding nine to 12 fields) to give a composite plan. Each planning approach was tested on six patients (ages 3-10 years). A pediatric radiation oncologist assessed clinical acceptability of each autoplan. RESULTS: The autocontoured structures' average Dice similarity coefficient ranged from .65 to .98. The average V95 for the brain/spinal canal for single, extended, and multi-field spine configurations was 99.9% ± 0.06%/99.9% ± 0.10%, 99.9% ± 0.07%/99.4% ± 0.30%, and 99.9% ± 0.06%/99.4% ± 0.40%, respectively. The average maximum dose across all field configurations to the brainstem, eyes (L/R), lenses (L/R), and spinal cord were 23.7 ± 0.08, 24.1 ± 0.28, 13.3 ± 5.27, and 25.5 ± 0.34 Gy, respectively (prescription = 23.4 Gy/13 fractions). Of the 18 plans tested, all were scored as clinically acceptable as-is or clinically acceptable with minor, time-efficient edits preferred or required. No plans were scored as clinically unacceptable. CONCLUSION: The autoplanning tool successfully generated pediatric CSI plans for varying patient lengths in 3.50 ± 0.4 minutes on average, indicating potential for an efficient planning aid in a resource-constrained settings.

Clinical acceptability of fully automated external beam radiotherapy for cervical cancer with three different beam delivery techniques.

PURPOSE: To fully automate CT-based cervical cancer radiotherapy by automating contouring and planning for three different treatment techniques. METHODS: We automated three different radiotherapy planning techniques for locally advanced cervical cancer: 2D 4-field-box (4-field-box), 3D conformal radiotherapy (3D-CRT), and volumetric modulated arc therapy (VMAT). These auto-planning algorithms were combined with a previously developed auto-contouring system. To improve the quality of the 4-field-box and 3D-CRT plans, we used an in-house, field-in-field (FIF) automation program. Thirty-five plans were generated for each technique on CT scans from multiple institutions and evaluated by five experienced radiation oncologists from three different countries. Every plan was reviewed by two of the five radiation oncologists and scored using a 5-point Likert scale. RESULTS: Overall, 87%, 99%, and 94% of the automatically generated plans were found to be clinically acceptable without modification for the 4-field-box, 3D-CRT, and VMAT plans, respectively. Some customizations of the FIF configuration were necessary on the basis of radiation oncologist preference. Additionally, in some cases, it was necessary to renormalize the plan after it was generated to satisfy radiation oncologist preference. CONCLUSION: Approximately, 90% of the automatically generated plans were clinically acceptable for all three planning techniques. This fully automated planning system has been implemented into the radiation planning assistant for further testing in resource-constrained radiotherapy departments in low- and middle-income countries.

Automatic contouring QA method using a deep learning-based autocontouring system.

PURPOSE: To determine the most accurate similarity metric when using an independent system to verify automatically generated contours. METHODS: A reference autocontouring system (primary system to create clinical contours) and a verification autocontouring system (secondary system to test the primary contours) were used to generate a pair of 6 female pelvic structures (UteroCervix [uterus + cervix], CTVn [nodal clinical target volume (CTV)], PAN [para-aortic lymph nodes], bladder, rectum, and kidneys) on 49 CT scans from our institution and 38 from other institutions. Additionally, clinically acceptable and unacceptable contours were manually generated using the 49 internal CT scans. Eleven similarity metrics (volumetric Dice similarity coefficient (DSC), Hausdorff distance, 95% Hausdorff distance, mean surface distance, and surface DSC with tolerances from 1 to 10 mm) were calculated between the reference and the verification autocontours, and between the manually generated and the verification autocontours. A support vector machine (SVM) was used to determine the threshold that separates clinically acceptable and unacceptable contours for each structure. The 11 metrics were investigated individually and in certain combinations. Linear, radial basis function, sigmoid, and polynomial kernels were tested using the combinations of metrics as inputs for the SVM. RESULTS: The highest contouring error detection accuracies were 0.91 for the UteroCervix, 0.90 for the CTVn, 0.89 for the PAN, 0.92 for the bladder, 0.95 for the rectum, and 0.97 for the kidneys and were achieved using surface DSCs with a thickness of 1, 2, or 3 mm. The linear kernel was the most accurate and consistent when a combination of metrics was used as an input for the SVM. However, the best model accuracy from the combinations of metrics was not better than the best model accuracy from a surface DSC as an input. CONCLUSIONS: We distinguished clinically acceptable contours from clinically unacceptable contours with an accuracy higher than 0.9 for the targets and critical structures in patients with cervical cancer; the most accurate similarity metric was surface DSC with a thickness of 1, 2, or 3 mm.

Bridging the Radiotherapy Education Gap in Africa: Lessons Learnt from the Cape Town Access to Care Training Programme Over the Past 5 Years (2015-2019).

The role of radiotherapy (RT) in cancer care is well described, with a clear correlation between access to radiotherapy and overall survival. Cancer mortality rates in Africa are substantially higher than those of the rest of the world, which may be partly attributed to lack of RT access and insufficient human resources. The Access to Care (A2C) Cape Town RT training programme was created in 2014 with the aim of supplementing practical RT training in the region, focusing on clinics moving from 2 to 3D conformal radiotherapy (3DCRT). The programme makes use of hybrid teaching methods, including pre-course e-learning followed by 17 on-site days of free-thinking design exercises, didactic learning, hands-on treatment planning computer sessions (39% of total teaching time), virtual simulation training and departmental demonstration sessions. Email support is offered to all teams for 3 months after each course to develop clinical protocols. Thirteen teams (radiation oncologist, medical physicist and radiation therapy technologist) from Africa attended the course between 2015 and 2019, with additional participants from seven South African and four international centres. E-learning done on the LäraNära training platform was only successful once formal progress tracking was introduced in 2019 (34% vs. 76% test completion rate). Delays between course attendance and initial clinical use of equipment proved to be detrimental to knowledge retention, with some centres having to send a second team for training. The course will be modified for remote teaching in 2021, to make provision for the global changes in travel due to Covid-19.

A novel external beam radiotherapy method for cervical cancer patients using virtual straight or bending boost areas; an in-silico feasibility study.

AIM: To investigate the potential role of a novel spatially fractionated radiation therapy (SFRT) method where heterogeneous dose patterns are created in target areas with virtual rods, straight or curving, of variable position, diameter, separation and alignment personalised to a patient's anatomy. The images chosen for this study were CT scans acquired for the external beam part of radiotherapy. METHODS: Ten patients with locally advanced cervical cancer were retrospectively investigated with SFRT. The dose prescription was 30 Gy in 5 fractions to 90% target volume coverage. Peak-and-valley (SFRT_1) and peak-only (SFRT_2) strategies were applied to generate the heterogeneous dose distributions. The planning objectives for the target (CTV) were D90% ≥ 30 Gy, V45Gy ≥ 50-55% and V60Gy ≥ 30%. The planning objectives for the organs at risk (OAR) were: D2cm3 ≤ 23.75 Gy, 17.0 Gy, 19.5 Gy, 17.0 Gy for the bladder, rectum, sigmoid and bowel, respectively. The plan comparison was performed employing the quantitative analysis of the dose-volume histograms. RESULTS: The D2cm3 was 22.4 ± 2.0 (22.6 ± 2.1) and 13.9 ± 2.9 (13.2 ± 3.0) for the bladder and the rectum for SFRT_1 (SFRT_2). The results for the sigmoid and the bowel were 2.6 ± 3.1 (2.8 ± 3.0) and 9.1 ± 5.9 (9.7 ± 7.3), respectively. The hotspots in the target volume were V45Gy = 43.1 ± 7.5% (56.6 ± 5.6%) and V60Gy = 15.4 ± 5.6% (26.8 ± 6.6%) for SFRT_1 (SFRT_2). To account for potential uncertainties in the positioning, the dose prescription could be escalated to D90% = 33-35 Gy to the CTV without compromising any constraints to the OARs CONCLUSION: In this dosimetric study, the proposed novel planning technique for boosting the cervix uteri was associated with high-quality plans, respecting constraints for the organs at risk and approaching the level of dose heterogeneity achieved with routine brachytherapy. Based on a sample of 10 patients, the results are promising and might lead to a phase I clinical trial.

Clinical Acceptability of Automated Radiation Treatment Planning for Head and Neck Cancer Using the Radiation Planning Assistant.

PURPOSE: Radiation treatment planning for head and neck cancer is a complex process with much variability; automated treatment planning is a promising option to improve plan quality and efficiency. This study compared radiation plans generated from a fully automated radiation treatment planning system to plans generated manually that had been clinically approved and delivered. METHODS AND MATERIALS: The study cohort consisted of 50 patients treated by a specialized head and neck cancer team at a tertiary care center. An automated radiation treatment planning system, the Radiation Planning Assistant, was used to create autoplans for all patients using their original, approved contours. Common dose-volume histogram (DVH) criteria were used to compare the quality of autoplans to the clinical plans. Fourteen radiation oncologists, each from a different institution, then reviewed and compared the autoplans and clinical plans in a blinded fashion. RESULTS: Autoplans and clinical plans were very similar with regard to DVH metrics for coverage and critical structure constraints. Physician reviewers found both the clinical plans and autoplans acceptable for use; overall, 78% of the clinical plans and 88% of the autoplans were found to be usable as is (without any edits). When asked to choose which plan would be preferred for approval, 27% of physician reviewers selected the clinical plan, 47% selected the autoplan, 25% said both were equivalent, and 0% said neither. Hence, overall, 72% of physician reviewers believed the autoplan or either the clinical or autoplan was preferable. CONCLUSIONS: Automated radiation treatment planning creates consistent, clinically acceptable treatment plans that meet DVH criteria and are found to be appropriate on physician review.

Automatic contouring system for cervical cancer using convolutional neural networks.

PURPOSE: To develop a tool for the automatic contouring of clinical treatment volumes (CTVs) and normal tissues for radiotherapy treatment planning in cervical cancer patients. METHODS: An auto-contouring tool based on convolutional neural networks (CNN) was developed to delineate three cervical CTVs and 11 normal structures (seven OARs, four bony structures) in cervical cancer treatment for use with the Radiation Planning Assistant, a web-based automatic plan generation system. A total of 2254 retrospective clinical computed tomography (CT) scans from a single cancer center and 210 CT scans from a segmentation challenge were used to train and validate the CNN-based auto-contouring tool. The accuracy of the tool was evaluated by calculating the Sørensen-dice similarity coefficient (DSC) and mean surface and Hausdorff distances between the automatically generated contours and physician-drawn contours on 140 internal CT scans. A radiation oncologist scored the automatically generated contours on 30 external CT scans from three South African hospitals. RESULTS: The average DSC, mean surface distance, and Hausdorff distance of our CNN-based tool were 0.86/0.19 cm/2.02 cm for the primary CTV, 0.81/0.21 cm/2.09 cm for the nodal CTV, 0.76/0.27 cm/2.00 cm for the PAN CTV, 0.89/0.11 cm/1.07 cm for the bladder, 0.81/0.18 cm/1.66 cm for the rectum, 0.90/0.06 cm/0.65 cm for the spinal cord, 0.94/0.06 cm/0.60 cm for the left femur, 0.93/0.07 cm/0.66 cm for the right femur, 0.94/0.08 cm/0.76 cm for the left kidney, 0.95/0.07 cm/0.84 cm for the right kidney, 0.93/0.05 cm/1.06 cm for the pelvic bone, 0.91/0.07 cm/1.25 cm for the sacrum, 0.91/0.07 cm/0.53 cm for the L4 vertebral body, and 0.90/0.08 cm/0.68 cm for the L5 vertebral bodies. On average, 80% of the CTVs, 97% of the organ at risk, and 98% of the bony structure contours in the external test dataset were clinically acceptable based on physician review. CONCLUSIONS: Our CNN-based auto-contouring tool performed well on both internal and external datasets and had a high rate of clinical acceptability.

Concrete density estimation of linac bunker walls using impact-echo testing.

PURPOSE: To estimate the concrete density of a newly constructed bunker using impact-echo testing prior to the installation of the linear accelerator. METHODS: A newly constructed bunker showed visible honeycombing after the removal of the construction formwork. Impact-echo testing, which is based on the propagation and reflection of elastic waves in solids, was applied to confirm the bunker shielding integrity. A mechanical impact on the bunker wall generates a stress pulse, which propagates through the wall and is reflected or refracted by voids or changes in material characteristics such as density. Surface displacements caused by the reflected waves are recorded by a transducer, located near the impact point. The resulting displacement-time curves are analysed in the frequency domain for anomalies. The dominant frequencies are related to the depths from which stress waves are reflected within the structure. If the dynamic elastic modulus and Poisson ratio of the concrete are known, then the measured velocity of the so-called P-wave can be related to the concrete density. RESULTS: Validation measurements on a wall with known concrete density gave an estimate within 3% of the true density. Measured velocities on the honeycombed wall ranged from 3750 m/s to 4300 m/s, corresponding to densities of 2894 kg/m3 and 2201 kg/m3 respectively, with the majority of estimated densities ranging from 2307 kg/m3 to 2544 kg/m3. A radiation survey after the installation of the linear accelerator confirmed adequate shielding. CONCLUSION: Impact-echo testing presents a viable solution to confirm bunker shielding integrity before the installation of a linac.

Detoxification of the Fumonisin Mycotoxins in Maize: An Enzymatic Approach.

Enzymatic detoxification has become a promising approach for control of mycotoxins postharvest in grains through modification of chemical structures determining their toxicity. In the present study fumonisin esterase FumD (EC 3.1.1.87) (FUMzyme®; BIOMIN, Tulln, Austria), hydrolysing fumonisin (FB) mycotoxins by de-esterification, was utilised to develop an enzymatic reduction method in a maize kernel enzyme incubation mixture. Efficacy of the FumD FB reduction method in "low" and "high" FB contaminated home-grown maize was compared by monitoring FB1 hydrolysis to the hydrolysed FB1 (HFB1) product utilising a validated LC-MS/MS analytical method. The method was further evaluated in terms of enzyme activity and treatment duration by assessing enzyme kinetic parameters and the relative distribution of HFB1 between maize kernels and the residual aqueous environment. FumD treatments resulted in significant reduction (≥80%) in "low" (≥1000 U/L, p < 0.05) and "high" (100 U/L, p < 0.05; ≥1000 U/L, p < 0.0001) FB contaminated maize after 1 h respectively, with an approximate 1:1 µmol conversion ratio of FB1 into the formation of HFB1. Enzyme kinetic parameters indicated that, depending on the activity of FumD utilised, a significantly (p < 0.05) higher FB1 conversion rate was noticed in "high" FB contaminated maize. The FumD FB reduction method in maize could find application in commercial maize-based practices as well as in communities utilising home-grown maize as a main dietary staple and known to be exposed above the tolerable daily intake levels.

Rural Subsistence Maize Farming in South Africa: Risk Assessment and Intervention models for Reduction of Exposure to Fumonisin Mycotoxins.

Maize is a staple crop in rural subsistence regions of southern Africa, is mainly produced for direct household consumption and is often contaminated with high levels of mycotoxins. Chronic exposure to mycotoxins is a risk factor for human diseases as it is implicated in the development of cancer, neural tube defects as well as stunting in children. Although authorities may set maximum levels, these regulations are not effective in subsistence farming communities. As maize is consumed in large quantities, exposure to mycotoxins will surpass safe levels even where the contamination levels are below the regulated maximum levels. It is clear that the lowering of exposure in these communities requires an integrated approach. Detailed understanding of agricultural practices, mycotoxin occurrence, climate change/weather patterns, human exposure and risk are warranted to guide adequate intervention programmes. Risk communication and creating awareness in affected communities are also critical. A range of biologically based products for control of mycotoxigenic fungi and mycotoxins in maize have been developed and commercialised. Application of these methods is limited due to a lack of infrastructure and resources. Other challenges regarding integration and sustainability of technological and community-based mycotoxin reduction strategies include (i) food security, and (ii) the traditional use of mouldy maize.

Automated treatment planning of postmastectomy radiotherapy.

PURPOSE: Breast cancer is the most common cancer in women globally and radiation therapy is a cornerstone of its treatment. However, there is an enormous shortage of radiotherapy staff, especially in low- and middle-income countries. This shortage could be ameliorated through increased automation in the radiation treatment planning process, which may reduce the workload on radiotherapy staff and improve efficiency in preparing radiotherapy treatments for patients. To this end, we sought to create an automated treatment planning tool for postmastectomy radiotherapy (PMRT). METHODS: Algorithms to automate every step of PMRT planning were developed and integrated into a commercial treatment planning system. The only required inputs for automated PMRT planning are a planning computed tomography scan, a plan directive, and selection of the inferior border of the tangential fields. With no other human input, the planning tool automatically creates a treatment plan and presents it for review. The major automated steps are (a) segmentation of relevant structures (targets, normal tissues, and other planning structures), (b) setup of the beams (tangential fields matched with a supraclavicular field), and (c) optimization of the dose distribution by using a mix of high- and low-energy photon beams and field-in-field modulation for the tangential fields. This automated PMRT planning tool was tested with ten computed tomography scans of patients with breast cancer who had received irradiation of the left chest wall. These plans were assessed quantitatively using their dose distributions and were reviewed by two physicians who rated them on a three-tiered scale: use as is, minor changes, or major changes. The accuracy of the automated segmentation of the heart and ipsilateral lung was also assessed. Finally, a plan quality verification tool was tested to alert the user to any possible deviations in the quality of the automatically created treatment plans. RESULTS: The automatically created PMRT plans met the acceptable dose objectives, including target coverage, maximum plan dose, and dose to organs at risk, for all but one patient for whom the heart objectives were exceeded. Physicians accepted 50% of the treatment plans as is and required only minor changes for the remaining 50%, which included the one patient whose plan had a high heart dose. Furthermore, the automatically segmented contours of the heart and ipsilateral lung agreed well with manually edited contours. Finally, the automated plan quality verification tool detected 92% of the changes requested by physicians in this review. CONCLUSIONS: We developed a new tool for automatically planning PMRT for breast cancer, including irradiation of the chest wall and ipsilateral lymph nodes (supraclavicular and level III axillary). In this initial testing, we found that the plans created by this tool are clinically viable, and the tool can alert the user to possible deviations in plan quality. The next step is to subject this tool to prospective testing, in which automatically planned treatments will be compared with manually planned treatments.

A risk assessment of automated treatment planning and recommendations for clinical deployment.

PURPOSE: To assess the risk of failure of a recently developed automated treatment planning tool, the radiation planning assistant (RPA), and to determine the reduction in these risks with implementation of a quality assurance (QA) program specifically designed for the RPA. METHODS: We used failure mode and effects analysis (FMEA) to assess the risk of the RPA. The steps involved in the workflow of planning a four-field box treatment of cervical cancer with the RPA were identified. Then, the potential failure modes at each step and their causes were identified and scored according to their likelihood of occurrence, severity, and likelihood of going undetected. Additionally, the impact of the components of the QA program on the detectability of the failure modes was assessed. The QA program was designed to supplement a clinic's standard QA processes and consisted of three components: (a) automatic, independent verification of the results of automated planning; (b) automatic comparison of treatment parameters to expected values; and (c) guided manual checks of the treatment plan. A risk priority number (RPN) was calculated for each potential failure mode with and without use of the QA program. RESULTS: In the RPA automated treatment planning workflow, we identified 68 potential failure modes with 113 causes. The average RPN was 91 without the QA program and 68 with the QA program (maximum RPNs were 504 and 315, respectively). The reduction in RPN was due to an improvement in the likelihood of detecting failures, resulting in lower detectability scores. The top-ranked failure modes included incorrect identification of the marked isocenter, inappropriate beam aperture definition, incorrect entry of the prescription into the RPA plan directive, and lack of a comprehensive plan review by the physician. CONCLUSIONS: Using FMEA, we assessed the risks in the clinical deployment of an automated treatment planning workflow and showed that a specialized QA program for the RPA, which included automatic QA techniques, improved the detectability of failures, reducing this risk. However, some residual risks persisted, which were similar to those found in manual treatment planning, and human error remained a major cause of potential failures. Through the risk analysis process, we identified three key aspects of safe deployment of automated planning: (a) user training on potential failure modes; (b) comprehensive manual plan review by physicians and physicists; and (c) automated QA of the treatment plan.

Fully Automatic Treatment Planning for External-Beam Radiation Therapy of Locally Advanced Cervical Cancer: A Tool for Low-Resource Clinics.

PURPOSE: The purpose of this study was to validate a fully automatic treatment planning system for conventional radiotherapy of cervical cancer. This system was developed to mitigate staff shortages in low-resource clinics. METHODS: In collaboration with hospitals in South Africa and the United States, we have developed the Radiation Planning Assistant (RPA), which includes algorithms for automating every step of planning: delineating the body contour, detecting the marked isocenter, designing the treatment-beam apertures, and optimizing the beam weights to minimize dose heterogeneity. First, we validated the RPA retrospectively on 150 planning computed tomography (CT) scans. We then tested it remotely on 14 planning CT scans at two South African hospitals. Finally, automatically planned treatment beams were clinically deployed at our institution. RESULTS: The automatically and manually delineated body contours agreed well (median mean surface distance, 0.6 mm; range, 0.4 to 1.9 mm). The automatically and manually detected marked isocenters agreed well (mean difference, 1.1 mm; range, 0.1 to 2.9 mm). In validating the automatically designed beam apertures, two physicians, one from our institution and one from a South African partner institution, rated 91% and 88% of plans acceptable for treatment, respectively. The use of automatically optimized beam weights reduced the maximum dose significantly (median, -1.9%; P < .001). Of the 14 plans from South Africa, 100% were rated clinically acceptable. Automatically planned treatment beams have been used for 24 patients with cervical cancer by physicians at our institution, with edits as needed, and its use is ongoing. CONCLUSION: We found that fully automatic treatment planning is effective for cervical cancer radiotherapy and may provide a reliable option for low-resource clinics. Prospective studies are ongoing in the United States and are planned with partner clinics.