A current perspective on stereotactic body radiation therapy for pancreatic cancer.

Pancreatic cancer is a formidable malignancy with poor outcomes. The majority of patients are unable to undergo resection, which remains the only potentially curative treatment option. The management of locally advanced (unresectable) pancreatic cancer is controversial; however, treatment with either chemotherapy or chemoradiation is associated with high rates of local tumor progression and metastases development, resulting in low survival rates. An emerging local modality is stereotactic body radiation therapy (SBRT), which uses image-guided, conformal, high-dose radiation. SBRT has demonstrated promising local control rates and resultant quality of life with acceptable rates of toxicity. Over the past decade, increasing clinical experience and data have supported SBRT as a local treatment modality. Nevertheless, additional research is required to further evaluate the role of SBRT and improve upon the persistently poor outcomes associated with pancreatic cancer. This review discusses the existing clinical experience and technical implementation of SBRT for pancreatic cancer and highlights the directions for ongoing and future studies

Interrater Reliability in Toxicity Identification: Limitations of Current Standards.

PurposeThe National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) v5.0 is the standard for oncology toxicity encoding and grading, despite limited validation. We assessed interrater reliability (IRR) in multireviewer toxicity identification.Methods and materialsTwo reviewers independently reviewed 100 randomly selected notes for weekly on-treatment visits during radiation therapy from the electronic health record. Discrepancies were adjudicated by a third reviewer for consensus. Term harmonization was performed to account for overlapping symptoms in CTCAE. IRR was assessed based on unweighted and weighted Cohen's kappa coefficients.ResultsBetween reviewers, the unweighted kappa was 0.68 (95% confidence interval, 0.65-0.71) and the weighted kappa was 0.59 (0.22-1.00). IRR was consistent between symptoms noted as present or absent with a kappa of 0.6 (0.66-0.71) and 0.6 (0.65-0.69), respectively.ConclusionsSignificant discordance suggests toxicity identification, particularly retrospectively, is a complex and error-prone task. Strategies to minimize IRR, including training and simplification of the CTCAE criteria, should be considered in trial design and future terminologies

Primary Meningeal Rhabdomyosarcoma

Primary meningeal rhabdomyosarcoma is a rare primary brain malignancy, with scant case reports. While most reports of primary intracranial rhabdomyosarcoma occur in pediatric patients, a handful of cases in adult patients have been reported in the medical literature. We report the case of a 44-year-old male who developed primary meningeal rhabdomyosarcoma. After developing episodes of right lower extremity weakness, word finding difficulty, and headaches, a brain magnetic resonance imaging (MRI) demonstrated a vertex lesion with radiographic appearance of a meningeal-derived tumor. Subtotal surgical resection was performed due to sagittal sinus invasion and initial pathology was interpreted as an anaplastic meningioma. Re-review of pathology demonstrated rhabdomyosarcoma negative for alveolar translocation t(2;13). Staging studies revealed no evidence of disseminated disease. He was treated with stereotactic radiotherapy with concurrent temozolamide to be followed by vincristine, actinomycin-D, and cyclophosphamide (VAC) systemic therapy

Health Care Cost Reductions with Machine Learning-Directed Evaluations during Radiation Therapy - An Economic Analysis of a Randomized Controlled Study.

BACKGROUND: Machine learning (ML) may cost-effectively direct health care by identifying patients most likely to benefit from preventative interventions to avoid negative and expensive outcomes. System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT; NCT04277650) was a single-institution, randomized controlled study in which electronic health record-based ML accurately identified patients at high risk for acute care (emergency visit or hospitalization) during radiotherapy (RT) and targeted them for supplemental clinical evaluations. This ML-directed intervention resulted in decreased acute care utilization. Given the limited prospective data showing the ability of ML to direct interventions cost-efficiently, an economic analysis was performed. METHODS: A post hoc economic analysis was conducted of SHIELD-RT that included RT courses from January 7, 2019, to June 30, 2019. ML-identified high-risk courses (≥10% risk of acute care during RT) were randomized to receive standard of care weekly clinical evaluations with ad hoc supplemental evaluations per clinician discretion versus mandatory twice-weekly evaluations. The primary outcome was difference in mean total medical costs during and 15 days after RT. Acute care costs were obtained via institutional cost accounting. Physician and intervention costs were estimated via Medicare and Medicaid data. Negative binomial regression was used to estimate cost outcomes after adjustment for patient and disease factors. RESULTS: A total of 311 high-risk RT courses among 305 patients were randomized to the standard (n=157) or the intervention (n=154) group. Unadjusted mean intervention group supplemental visit costs were $155 per course (95$142 to $168). The intervention group had fewer acute care visits per course (standard, 0.47; intervention, 0.31; P=0.04). Total mean adjusted costs were$3110 per course for the standard group and $1494 for the intervention group (difference in means,$1616 [95% confidence interval, $1450 to$1783]; P=0.03). CONCLUSIONS: In this economic analysis of a randomized controlled, health care ML study, mandatory supplemental evaluations for ML-identified high-risk patients were associated with both reduced total medical costs and improved clinical outcomes. Further study is needed to determine whether economic results are generalizable. (Funded in part by The Duke Endowment, The Conquer Cancer Foundation, the Duke Department of Radiation Oncology, and the National Cancer Institute of the National Institutes of Health [R01CA277782]; ClinicalTrials.gov number, NCT04277650.)

Crop Updates 2002 - Lupins

This session covers twenty four papers from different authors: LUPIN INDUSTRY ISSUES AND RESEARCH DIRECTIONS ACKNOWLEDGMENTS Amelia McLarty LUPIN CONVENOR DEPARTMENT OF AGRICULTURE VARIETIES 1. Evaluation of lupinus mutabilis in Western Australia, Bob French, Laurie Wahlsten and Martin Harries, Department of Agriculture 2. Adaption of restricted-branching lupins in short-growing season environments, Bob French, Laurie Wahlsten, Department of Agriculture ESTABLISHMENT 3. Moisture delving for better lupin establishment, Dr Paul Blackwell, Department of Agriculture 4. Lupins, tramlines, 600mm rows, rolling and shield spraying … a good result in a dry season! Paul Blackwell and Mike Collins, Department of Agriculture 5. Lupin wider row spacing data and observations, Bill CrabtreeA, Geoff FosberyB, Angie RoeB, Mike CollinsCand Matt BeckettA,AWANTFA, BFarm Focus Consultants and CDepartment of Agriculture NUTRITION 6. Lupin genotypes respond differently to potash, Bob French and Laurie Wahlsten, Department of Agriculture 7. Consequence of radish competition on lupin nutrients in a wheat-lupin rotation, Abul Hashem and Nerys Wilkins, Department of Agriculture 8. Consequence of ryegrass competition on lupin nutrients in a wheat-lupin rotation, Abul Hashem and Nerys Wilkins, Department of Agriculture PESTS AND DISEASES 9. Fungicide sprays for control of lupin anthracnose, Geoff Thomas and Ken Adcock, Department of Agriculture 10. Estimated yield losses in lupin varieties from sowing anthracnose infected seed, Geoff Thomas, Department of Agriculture 11. Effect of variety and environment (northern and southern wheatbelt) on yield losses in lupins due to anthracnose, Geoff Thomas and Ken Adcock, Department of Agriculture, 12. A decision support system for the control of aphids and CMV in lupin crops, Debbie Thackray, Jenny Hawkes and Roger Jones, Centre for Legumes in Mediterranean Agriculture and Department of Agriculture 13. Integrated management strategies for virus diseases of lupin, Roger Jones, Crop Improvement Institute, Department of Agriculture, and Centre for Legumes in Mediterranean Agriculture, University of WA 14. Quantifying yield losses caused by the non-necrotic strain of BYMV in lupin, Roger Jones and Brenda Coutts, Department of Agriculture, and Centre for Legumes in Mediterranean Agriculture 15. Screening for pod resistance to phomopsis in various lupin species, Manisha Shankar1, Mark Sweetingham1&2and Bevan Buirchell2 1Co-operative Research Centre for Legumes in Mediterranean Agriculture, The University of Western Australia, 2 Department of Agriculture 16. Lupin disease diagnostics, Nichole Burges and Dominie Wright, Department of Agriculture QUALITY AND MARKET DEVELOPMENT 17. To GM or not to GM pulses – that is the question, Dr Susan J. Barker, The University of Western Australia 18. Towards a management package for grain protein in lupins, Bob French, Senior Research Officer, Department of Agriculture 19. Yield and seed protein response to foliar application of N among lupin genotypes, Jairo A Palta1&2, Bob French2&3and Neil C Turner1&2 , 1 CSIRO Plant Industry, Floreat Park, 2 CLIMA, University of Western Australia,3Department of Agriculture 20. Foliar nitrogen application to improve protein content in narrow-leafed lupin, Martin Harries, Bob French, Laurie Wahlsten, Department of Agriculture, Matt Evans, CSBP 21. Effect of time of swathing of lupins on grain protein content, Martin Harries, Department of Agriculture 22. Putting a value on protein premiums for the animal feed industries: Aquaculture, Brett Glencross and John Curnow, Department of Fisheries, Wayne Hawkins, Department of Agriculture 23. Progress in selecting for reduced seed hull and pod wall in lupin, Jon C. Clements, CLIMA, University of Western Australia 24. Contact details for principal author

Predicting Emergency Visits and Hospital Admissions During Radiation and Chemoradiation: An Internally Validated Pretreatment Machine Learning Algorithm.

PurposePatients undergoing radiotherapy (RT) or chemoradiotherapy (CRT) may require emergency department evaluation or hospitalization. Early identification may direct preventative supportive care, improving outcomes and reducing health care costs. We developed and evaluated a machine learning (ML) approach to predict these events.MethodsA total of 8,134 outpatient courses of RT and CRT from a single institution from 2013 to 2016 were identified. Extensive pretreatment data were programmatically extracted and processed from the electronic health record (EHR). Training and internal validation cohorts were randomly generated (3:1 ratio). Gradient tree boosting (GTB), random forest, support vector machine, and least absolute shrinkage and selection operator logistic regression approaches were trained and internally validated based on area under receiver operating characteristic (AUROC) curve. The most predictive ML approach was also evaluated using only disease- and treatment-related factors to assess predictive gain of extensive EHR data.ResultsAll methods had high predictive accuracy, particularly GTB (validation AUROC, 0.798). Extensive EHR data beyond disease and treatment information improved accuracy (delta AUROC, 0.056). A Youden-based cutoff corresponded to validation sensitivity of 81.0% (175 of 216 courses with events) and specificity of 67.3% (1,218 of 1811 courses without events). Interpretability is an important advantage of GTB. Variable importance identified top predictive factors, including treatment (planned RT and systemic therapy), pretreatment encounters (emergency department visits and admissions in the year before treatment), vital signs (weight loss and pain score in the year before treatment), and laboratory values (albumin level at weeks before treatment).ConclusionML predicts emergency visits and hospitalization during cancer therapy. Incorporating predictions into clinical care algorithms may help direct personalized supportive care, improve quality of care, and reduce costs. A prospective trial investigating ML-assisted direction of increased clinical assessments during RT is planned

Predicting Emergency Visits and Hospital Admissions During Radiation and Chemoradiation: An Internally Validated Pretreatment Machine Learning Algorithm.

PurposePatients undergoing radiotherapy (RT) or chemoradiotherapy (CRT) may require emergency department evaluation or hospitalization. Early identification may direct preventative supportive care, improving outcomes and reducing health care costs. We developed and evaluated a machine learning (ML) approach to predict these events.MethodsA total of 8,134 outpatient courses of RT and CRT from a single institution from 2013 to 2016 were identified. Extensive pretreatment data were programmatically extracted and processed from the electronic health record (EHR). Training and internal validation cohorts were randomly generated (3:1 ratio). Gradient tree boosting (GTB), random forest, support vector machine, and least absolute shrinkage and selection operator logistic regression approaches were trained and internally validated based on area under receiver operating characteristic (AUROC) curve. The most predictive ML approach was also evaluated using only disease- and treatment-related factors to assess predictive gain of extensive EHR data.ResultsAll methods had high predictive accuracy, particularly GTB (validation AUROC, 0.798). Extensive EHR data beyond disease and treatment information improved accuracy (delta AUROC, 0.056). A Youden-based cutoff corresponded to validation sensitivity of 81.0% (175 of 216 courses with events) and specificity of 67.3% (1,218 of 1811 courses without events). Interpretability is an important advantage of GTB. Variable importance identified top predictive factors, including treatment (planned RT and systemic therapy), pretreatment encounters (emergency department visits and admissions in the year before treatment), vital signs (weight loss and pain score in the year before treatment), and laboratory values (albumin level at weeks before treatment).ConclusionML predicts emergency visits and hospitalization during cancer therapy. Incorporating predictions into clinical care algorithms may help direct personalized supportive care, improve quality of care, and reduce costs. A prospective trial investigating ML-assisted direction of increased clinical assessments during RT is planned

A current perspective on stereotactic body radiation therapy for pancreatic cancer.

Pancreatic cancer is a formidable malignancy with poor outcomes. The majority of patients are unable to undergo resection, which remains the only potentially curative treatment option. The management of locally advanced (unresectable) pancreatic cancer is controversial; however, treatment with either chemotherapy or chemoradiation is associated with high rates of local tumor progression and metastases development, resulting in low survival rates. An emerging local modality is stereotactic body radiation therapy (SBRT), which uses image-guided, conformal, high-dose radiation. SBRT has demonstrated promising local control rates and resultant quality of life with acceptable rates of toxicity. Over the past decade, increasing clinical experience and data have supported SBRT as a local treatment modality. Nevertheless, additional research is required to further evaluate the role of SBRT and improve upon the persistently poor outcomes associated with pancreatic cancer. This review discusses the existing clinical experience and technical implementation of SBRT for pancreatic cancer and highlights the directions for ongoing and future studies