PS1 - 189 A Universal Predictive Model for Dose Fall-Off in MLC-Based Stereotactic Brain Radiosurgery

Predictive modeling of dose fall-off in radiosurgery could assist in clinical decision-making when prescribing a treatment plan with minimized toxicity risk. The purpose of this study is to develop a predictive dose fall-off model. Materials/Methods: We retrospectively reviewed treatment plans from 257 patients (365 lesions) with total doses ranging from 20 to 35Gy in 5 fractions. For each plan, we measured both total volume of the external contour (EXT) and BrainMinusPTV (BMP) receiving P=20% to P=80% of the prescription dose. The model has form y=Fa(PTV)b+/-delta. y=volume of EXT or BMP (cc’s); a and b are curve-fitting coefficients; PTV=total planning target volume (cc’s); F is an adjustment factor (>1) to account for number of targets; delta is the 95% prediction band. F, a, b, and delta were modeled such that dose-fall can be forecast for any PTV and dose level. Results: The model coefficients were as follows: Coefficient EXT BMP a 19927(100×P)exp(-2) 17122(100×P)exp(-2) b 0.42(100×P)exp(0.17) 0.63 F -0.0156×(100×P)+2.5517 delta 384467×(100×P)exp(-2.3159) The table can be used to determine the model for any P from 20% to 80%. Example: the EXT receiving 50%, P=0.5, a=8.0, b=0.82, F=1.8, delta=45. Thus, EXT-50=8(PTV0.82) or 1.8×8(PTV0.82) for 1-3 or >3 targets, respectively,+/-45cc’s. The model was verified against published values of dose fall-off from linacs. Conclusion: A predictive dose fall-off model was generated for linac-based radiosurgery. The model can be used for quality assurance or for inter-institutional comparisons. Ongoing work is being conducted to extend the model to a SRS cones system.

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Deep Learning: A New Horizon for Personalized Treatment of Depression?

McGill Journal of Medicine ◽

10.26443/mjm.v16i1.99 ◽

2018 ◽

Vol 16 (1) ◽

Author(s):

David Benrimoh ◽

Robert Fratila ◽

Sonia Israel ◽

Kelly Perlman

Keyword(s):

Deep Learning ◽

Decision Aid ◽

Clinical Decision Making ◽

Clinical Decision ◽

Personalized Care ◽

Treatment Of Depression ◽

Number Of Patients ◽

Treatment Plans ◽

Patient Responses ◽

Deep Learning Model

Globally, depression affects 300 million people and is projected be the leading cause of disability by 2030. While different patients are known to benefit from different therapies, there is no principled way for clinicians to predict individual patient responses or side effect profiles. A form of machine learning based on artificial neural networks, deep learning, might be useful for generating a predictive model that could aid in clinical decision making. Such a model’s primary outcomes would be to help clinicians select the most effective treatment plans and mitigate adverse side effects, allowing doctors to provide greater personalized care to a larger number of patients. In this commentary, we discuss the need for personalization of depression treatment and how a deep learning model might be used to construct a clinical decision aid.

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P83 Improving patient care through a collaborative effort in the Leeds Combined Psoriatic Service: an effective provision of multi-specialty input

Rheumatology ◽

10.1093/rheumatology/keaa111.081 ◽

2020 ◽

Vol 59 (Supplement_2) ◽

Author(s):

Claire Vandevelde ◽

Kate Harnden ◽

Jane Freeston ◽

Andrew Barr ◽

Kave Shams ◽

...

Keyword(s):

Patient Satisfaction ◽

Clinical Decision Making ◽

Treatment Plan ◽

Clinical Decision ◽

Fine Tuning ◽

Satisfaction Survey ◽

Tnf Inhibitor ◽

Patient Satisfaction Survey ◽

Dermatology Clinic ◽

Nice Guidance

Abstract Background NICE guidance (NG65) endorses the need for a multi-specialist approach in spondyloarthritis. The Leeds Combined Psoriatic Service has been running rheumatology and dermatology clinics in parallel since 2011, where patients with psoriatic arthritis (PsA) and psoriasis from both services are reviewed on request by both teams. Methods Prospective survey to assess the potential benefits of the current combined review over a 17-month period (May 2018-September 2019). In addition, a patient satisfaction survey was performed in the rheumatology setup. Results In a standard month, 120 patients with PsA and 180 with psoriasis are reviewed in each clinic. A total of 136 combined consultations took place during this study period with dermatologists reviewing rheumatology patients on 87 occasions and rheumatology reviewing dermatology patients on 49 occasions. Overall, the combined review had a direct impact on the other specialty’s clinical treatment plan in 32% (44/136). This included altering csDMARDs (16%; 7/44), switching or starting a TNF inhibitor (20%; 9/44), new IL-17 inhibition (41%; 18/44); IL-23 inhibition (19%; 8/44), apremilast (5%; 2/44). Furthermore, 12 Rheumatology patients with PsA were able to access a biological medication for which they didn't fulfil NICE criteria or which is not yet approved for use in PsA, based on their skin involvement (1 secukinumab, 2 ixekizumab, 3 adalimumab, 1 rizankizumab, 5 guselkumab); 2 dermatology patients were able to access biological medication prescribed by rheumatology (1 adalimumab, 1 apremilast). Objectively, combined reviews avoided a separate out-patient appointment on 82 occasions averaging a saving of £21,080 to the CCG. A new rheumatological diagnosis was given to 18 people attending the dermatology clinic (4 spondyloarthritis; osteoarthritis, gout, fibromyalgia or mechanical joint pains) and 34 people were given a new skin diagnosis (3 new psoriasis, 9 fungal rash or possible psoriasis, 2 vasculitis, 17 eczema, rosacea or other). During the past year, 196 patients were recruited to clinical trials in rheumatology and 170 in dermatology conducted studies. The patient satisfaction survey was returned by 108 rheumatology patients. In total, 24% (26 of 108) reported benefitting from a dermatologist’s review whilst attending a rheumatology appointment. 70% of rheumatology patients with psoriasis, who were not already under dermatology, had either seen or would like to have seen a dermatologist whilst attending rheumatology clinic. Conclusion Combined clinics give clear patient benefits, with improved clinical decision making, enhanced diagnosis and access to therapies. In addition, they carry substantial savings for the CCGs and lead to high patient satisfaction. These specialised services support pioneering clinical practice and deliver cutting-edge care acting as hubs for translational research within the NHS, and as such should be eligible for high tariff commissioning to allow for fine tuning on the delivery of quality of care and cost control. Disclosures C. Vandevelde: None. K. Harnden: None. J. Freeston: None. A. Barr: None. K. Shams: None. P. Laws: None. H. Marzo-Ortega: None.

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Nanodosimetry-Based Plan Optimization for Particle Therapy

Computational and Mathematical Methods in Medicine ◽

10.1155/2015/908971 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13 ◽

Cited By ~ 11

Author(s):

Margherita Casiraghi ◽

Reinhard W. Schulte

Keyword(s):

Treatment Plan ◽

Target Volume ◽

Particle Therapy ◽

Optimization Strategy ◽

Plan Optimization ◽

Treatment Plan Optimization ◽

Treatment Plans ◽

Mixed Field ◽

Single Field ◽

Mixed Radiation Field

Treatment planning for particle therapy is currently an active field of research due uncertainty in how to modify physical dose in order to create a uniform biological dose response in the target. A novel treatment plan optimization strategy based on measurable nanodosimetric quantities rather than biophysical models is proposed in this work. Simplified proton and carbon treatment plans were simulated in a water phantom to investigate the optimization feasibility. Track structures of the mixed radiation field produced at different depths in the target volume were simulated with Geant4-DNA and nanodosimetric descriptors were calculated. The fluences of the treatment field pencil beams were optimized in order to create a mixed field with equal nanodosimetric descriptors at each of the multiple positions in spread-out particle Bragg peaks. For both proton and carbon ion plans, a uniform spatial distribution of nanodosimetric descriptors could be obtained by optimizing opposing-field but not single-field plans. The results obtained indicate that uniform nanodosimetrically weighted plans, which may also be radiobiologically uniform, can be obtained with this approach. Future investigations need to demonstrate that this approach is also feasible for more complicated beam arrangements and that it leads to biologically uniform response in tumor cells and tissues.

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Outpatient follow-up of nonoperative cerebral contusion and traumatic subarachnoid hemorrhage: does repeat head CT alter clinical decision-making?

Journal of Neurosurgery ◽

10.3171/2014.6.jns132204 ◽

2014 ◽

Vol 121 (4) ◽

pp. 944-949 ◽

Cited By ~ 11

Author(s):

Sebastian Rubino ◽

Rifat A. Zaman ◽

Caleb R. Sturge ◽

Jessica G. Fried ◽

Atman Desai ◽

...

Keyword(s):

Clinical Decision Making ◽

Treatment Plan ◽

Clinical Decision ◽

Neurological Symptoms ◽

Cerebral Contusion ◽

Head Ct ◽

Ct Findings ◽

Asymptomatic Patients ◽

Traumatic Subarachnoid Hemorrhage

Object Many neurosurgeons obtain repeat head CT at the first clinic follow-up visit for nonoperative cerebral contusion and traumatic subarachnoid hemorrhage (tSAH). The authors undertook a single-center, retrospective study to determine whether outpatient CT altered clinical decision-making. Methods The authors evaluated 173 consecutive adult patients admitted to their institution from April 2006 to August 2012 with an admission diagnosis of cerebral contusion or tSAH and at least 1 clinic follow-up visit with CT. Patients with epidural, subdural, aneurysmal subarachnoid, or intraventricular hemorrhage, and those who underwent craniotomy, were excluded. Patient charts were reviewed for new CT findings, new patient symptoms, and changes in treatment plan. Patients were stratified by neurological symptoms into 3 groups: 1) asymptomatic; 2) mild, nonspecific symptoms; and 3) significant symptoms. Mild, nonspecific symptoms included minor headaches, vertigo, fatigue, and mild difficulties with concentration, short-term memory, or sleep; significant symptoms included moderate to severe headaches, nausea, vomiting, focal neurological complaints, impaired consciousness, or new cognitive impairment evident on routine clinical examination. Results One hundred seventy-three patients met inclusion criteria, with initial clinic follow-up obtained within approximately 6 weeks. Of the 173 patients, 104 (60.1%) were asymptomatic, 68 patients (39.3%) had mild, nonspecific neurological symptoms, and 1 patient (1.0%) had significant neurological symptoms. Of the asymptomatic patients, 3 patients (2.9%) had new CT findings and 1 of these patients (1.0%) underwent a change in treatment plan because of these findings. This change involved an additional clinic appointment and CT to monitor a 12-mm chronic subdural hematoma that ultimately resolved without treatment. Of the patients with mild, nonspecific neurological symptoms, 6 patients (8.8%) had new CT findings and 3 of these patients (4.4%) underwent a change in treatment plan because of these findings; none of these patients required surgical intervention. The single patient with significant neurological symptoms did not have any new CT findings. Conclusions Repeat outpatient CT of asymptomatic patients after nonoperative cerebral contusion and tSAH is very unlikely to demonstrate significant new pathology. Given the cost and radiation exposure associated with CT, imaging should be reserved for patients with significant symptoms or focal findings on neurological examination.

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Clinical Validation and Treatment Plan Evaluation Based on Auto-Delineation of the Clinical Target Volume for Prostate Cancer Radiotherapy

10.21203/rs.3.rs-1226468/v1 ◽

2022 ◽

Author(s):

Jing Shen ◽

Yinjie TAO ◽

Hui GUAN ◽

Hongnan ZHEN ◽

Lei HE ◽

...

Keyword(s):

Prostate Cancer ◽

At Risk ◽

Organs At Risk ◽

Treatment Plan ◽

Statistical Significance ◽

Locally Advanced ◽

Target Volume ◽

Ct Scans ◽

Dice Similarity Coefficient ◽

Treatment Plans

Abstract Purpose Clinical target volumes (CTV) and organs at risk (OAR) could be auto-contoured to save workload. The goal of this study was to assess a convolutional neural network (CNN) for totally automatic and accurate CTV and OAR in prostate cancer, while also comparing anticipated treatment plans based on auto-contouring CTV to clinical plans. Methods From January 2013 to January 2019, 217 computed tomography (CT) scans of patients with locally advanced prostate cancer treated at our hospital were collected and analyzed. CTV and OAR were delineated with a deep learning based method, which named CUNet. The performance of this strategy was evaluated using the mean Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (95HD), and subjective evaluation. Treatment plans were graded using predetermined evaluation criteria, and % errors for clinical doses to the planned target volume (PTV) and organs at risk(OARs) were calculated. Results The defined CTVs had mean DSC and 95HD values of 0.84 and 5.04 mm, respectively. For one patient's CT scans, the average delineation time was less than 15 seconds. When CTV outlines from CUNetwere blindly chosen and compared to GT, the overall positive rate in clinicians A and B was 53.15% vs 46.85%, and 54.05% vs 45.95%, respectively (P>0.05), demonstrating that our deep machine learning model performed as good as or better than human demarcation Furthermore, 8 testing patients were chosen at random to design the predicted plan based on the auto-courtoring CTV and OAR, demonstrating acceptable agreement with the clinical plan: average absolute dose differences of D2, D50, D98, Dmean for PTV are within 0.74%, and average absolute volume differences of V45, V50 for OARs are within 3.4%. Without statistical significance (p>0.05), the projected findings are comparable to clinical truth. Conclusion The experimental results show that the CTV and OARs defined by CUNet for prostate cancer were quite close to the ground reality.CUNet has the potential to cut radiation oncologists' contouring time in half. When compared to clinical plans, the differences between estimated doses to CTV and OAR based on auto-courtoring were small, with no statistical significance, indicating that treatment planning for prostate cancer based on auto-courtoring has potential.

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Significance of different conformity indices for evaluation of radiosurgery treatment plans for vestibular schwannomas

Journal of Neurosurgery ◽

10.3171/jns.2004.101.supplement_3.0334 ◽

2004 ◽

pp. 334-340

Author(s):

Gunnar Surber ◽

Klaus Hamm ◽

Gabriele Kleinert

Keyword(s):

Normal Tissue ◽

Treatment Plan ◽

Target Volume ◽

Vestibular Schwannomas ◽

Content Type ◽

Target Volumes ◽

Treatment Plans ◽

Surgery Center ◽

Prescription Isodose ◽

Fine Print

Object. There are various kinds of conformity parameters currently in use, although several of them are limited and reflect only target volume coverage or normal tissue overdosage. Indices are reviewed with the goal of determining those that are most significant for the evaluation of radiosurgery treatment plans for patients with vestibular schwannoma, based on the authors' experience at the Novalis Shaped Beam Surgery Center. Methods. Fifty-five radiosurgery plans for patients with vestibular schwannomas (VSs) have been evaluated. In this paper the conformation number (CN) and dose-related CN (dCN) are evaluated, and a penalty for underdosed target volumes and overdosed normal tissue is incorporated. A strategy is discussed to apply these indices (CN and dCN) to define the optimal prescription isodose (PI). For a given radiosurgery treatment plan, permitting partial target underdosage may offer an improvement of the CN. Variations of different conformation indices have been calculated for varying prescription levels—for example, an isodose plan. The resulting graph for the CN is discussed in detail to illustrate its use in defining the optimal PI level. For the 55 cases of VSs reported on, the median CNmax result was 0.78. Conclusions. It is possible to achieve highly conformal dose distributions with Novalis radiosurgical system. The CN is the parameter of choice when evaluating radiosurgery treatment plans and scoring possible treatment plans. It takes into account both target underdosage and normal tissue overdosage and offers a valuable scoring parameter while avoiding false-perfect scores.

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A preliminary assessment of Internet-based nuclear telecardiology to support the clinical management of cardiac disease in a remote community

Journal of Telemedicine and Telecare ◽

10.1258/135763303322196411 ◽

2003 ◽

Vol 9 (1_suppl) ◽

pp. 69-71 ◽

Cited By ~ 1

Author(s):

Peter Tually ◽

Johan Janssen ◽

Simon Cowell ◽

John Walker

Keyword(s):

Clinical Decision Making ◽

Treatment Plan ◽

Processing System ◽

Clinical Decision ◽

Medical Intervention ◽

The Internet ◽

Laptop Computer ◽

Coronary Syndrome ◽

Perfusion Studies ◽

Use Of The Internet

summary A portable nuclear medicine (NM) processing system was established in Kalgoorlie and an acute myocardial perfusion scintigraphy (MPS) service was provided for the local regional hospital. After scanning the patient, the data were processed on a laptop computer and JPEG images were transmitted to a secure Web server. A secure email message, with the URL link enclosed and a provisional indication of normal or abnormal findings, was sent to the referring clinician from the NM facility. Use of the Internet allowed for a group consultation between the NM technician, the referrer and the cardiologist in Perth. During a three-month study period, 42 patients were referred for exclusion of acute coronary syndrome. Of these, 21 (50%) demonstrated abnormal perfusion studies, two of which were classified as requiring urgent medical intervention. Seventeen studies were normal (41%) and four (10%) were designated equivocal. There was an alteration in the treatment plan for 32 patients (76%), including four for whom admission or further investigation was deemed unwarranted. The results suggest that MPS findings, distributed via the Internet, allow for earlier risk stratification and have a direct affect on clinical decision making.

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Integrative Nursing Management of Anxiety

Integrative Nursing ◽

10.1093/med/9780190851040.003.0016 ◽

2018 ◽

pp. 221-239

Author(s):

Pamela Lusk ◽

Michelle Kahn-John

Keyword(s):

Symptom Management ◽

Clinical Decision Making ◽

Treatment Plan ◽

Treatment Options ◽

Management Strategies ◽

Clinical Decision ◽

Nursing Management ◽

Emotional Wellbeing ◽

Patient Centered

Anxiety is a sense of perceived threat to one’s physical safety or emotional wellbeing, and symptoms are experienced in body, mind, and spirit. This chapter focuses on anxiety as a normal reaction to stressors—external, internal, or existential. Everyone experiences anxiety, both trait anxiety and state anxiety at multiple points in their lives; however, prolonged and severe symptoms of anxiety can evolve into a clinical form of anxiety, such as generalized anxiety disorder, panic disorder, or posttraumatic stress disorder. This chapter reviews assessment of anxiety, including evidence-based screening measures and the patient-centered interview. Symptom identification and management incorporating integrative nursing principles are presented, including patient self-management strategies as well as provider-directed treatment options. In clinical decision-making for anxiety symptom management, concordance with the patient in establishing a treatment plan is emphasized. A case study highlights a patient-centered and culturally sensitive approach to the management of anxiety.

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Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling

BJR|Open ◽

10.1259/bjro.20190021 ◽

2019 ◽

Vol 1 (1) ◽

pp. 20190021 ◽

Cited By ~ 4

Author(s):

Yi Luo ◽

Huan-Hsin Tseng ◽

Sunan Cui ◽

Lise Wei ◽

Randall K. Ten Haken ◽

...

Keyword(s):

Machine Learning ◽

Clinical Decision Making ◽

Radiation Treatment ◽

Clinical Decision ◽

Black Box ◽

Prediction Performance ◽

Learning Approaches ◽

Treatment Plans ◽

The Relationship ◽

Review Current

Radiation outcomes prediction (ROP) plays an important role in personalized prescription and adaptive radiotherapy. A clinical decision may not only depend on an accurate radiation outcomes’ prediction, but also needs to be made based on an informed understanding of the relationship among patients’ characteristics, radiation response and treatment plans. As more patients’ biophysical information become available, machine learning (ML) techniques will have a great potential for improving ROP. Creating explainable ML methods is an ultimate task for clinical practice but remains a challenging one. Towards complete explainability, the interpretability of ML approaches needs to be first explored. Hence, this review focuses on the application of ML techniques for clinical adoption in radiation oncology by balancing accuracy with interpretability of the predictive model of interest. An ML algorithm can be generally classified into an interpretable (IP) or non-interpretable (NIP) (“black box”) technique. While the former may provide a clearer explanation to aid clinical decision-making, its prediction performance is generally outperformed by the latter. Therefore, great efforts and resources have been dedicated towards balancing the accuracy and the interpretability of ML approaches in ROP, but more still needs to be done. In this review, current progress to increase the accuracy for IP ML approaches is introduced, and major trends to improve the interpretability and alleviate the “black box” stigma of ML in radiation outcomes modeling are summarized. Efforts to integrate IP and NIP ML approaches to produce predictive models with higher accuracy and interpretability for ROP are also discussed.

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General practitioners' decision to refer patients to dietitians: insight into the clinical reasoning process

Australian Journal of Primary Health ◽

10.1071/py09024 ◽

2010 ◽

Vol 16 (2) ◽

pp. 147 ◽

Cited By ~ 15

Author(s):

Sylvia E. M. Pomeroy ◽

Robyn P. Cant

Keyword(s):

Decision Making ◽

General Practitioners ◽

Mixed Methods Research ◽

Clinical Decision Making ◽

Treatment Plan ◽

Clinical Decision ◽

Nutrition Intervention ◽

Enabling Factors ◽

Reasoning Process ◽

The Impact

The aim of this project was to describe general practitioners’ (GPs’) decision-making process for reducing nutrition risk in cardiac patients through referring a patient to a dietitian. The setting was primary care practices in Victoria. The method we employed was mixed methods research: in Study 1, 30 GPs were interviewed. Recorded interviews were transcribed and narratives analysed thematically. Study 2 involved a survey of statewide random sample of GPs. Frequencies and analyses of variance were used to explore the impact of demographic variables on decisions to refer. We found that the referral decision involved four elements: (i) synthesising management information; (ii) forecasting outcomes; (iii) planning management; and (iv) actioning referrals. GPs applied cognitive and collaborative strategies to develop a treatment plan. In Study 2, doctors (248 GPs, 30%) concurred with identified barriers/enabling factors for patients’ referral. There was no association between GPs’ sex, age or hours worked per week and referral factors. We conclude that a GP’s judgment to offer a dietetic referral to an adult patient is a four element reasoning process. Attention to how these elements interact may assist clinical decision making. Apart from the sole use of prescribed medications/surgical procedures for cardiac care, patients offered a dietetic referral were those who were considered able to commit to dietary change and who were willing to attend a dietetic consultation. Improvements in provision of patients’ nutrition intervention information to GPs are needed. Further investigation is justified to determine how to resolve this practice gap.

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