- Email: [email protected]
A Pilot Study to Improve Workflow in an Academic Radiation Oncology Department
Proceedings of the 51st Annual ASTRO Meeting same scan. A median surface volume for all users was calculated for each contour set. The fractional volume of the median volume uncontoured (Vmiss) was calculated for each user/session. Cumulative inter-user local SD from median volumes for each trial was assessed using Levene’s test for homogeneity of variance. Variance component estimation was performed with residual maximum likelihood (REML) analysis. Results: From 15 users, 60 contour sets were technically evaluable. The median Vmiss pre-randomization for GTV, CTVA, and CTVB for the atlas cohort was 0.14, 0.11, and 0.29, compared to 0.14, 0.33, and 0.33 for the control cohort; after atlas assignment, GTV, CTVA, and CTVB Vmiss were 0.9, 0.8, and 0.25 for atlas users, and 0.14, 0.28, and 0.3 for controls. A statistically significant difference in the contour local SD distribution was noted with randomization to atlas for CTVA and CTVB (both p \ 0.01), but not GTV (p = 0.6). However, REML analysis revealed that a markedly larger variance component was attributable to userassociated variation in Vmiss (70.7% for GTV, 73.4% CTVA, and 39.7% CTVB), compared to atlas utilization (\5% for all volumes). Conclusions: Preliminary results indicate that CTV delineation variability was more prominent within this test set than GTV delineation. User-related variability parameters were a larger component of variance atlas implementation. Data from this pilot study have implications regarding IMRT clinical trial protocol development. Author Disclosure: C.D. Fuller, None; J. Duppen, None; C.R.N. Rasch, None; L. Kachnic, None; S.J. Wang, None; D. Chang, None; K.A. Goodman, None; A.W. Katz, None; P. Okunieff, None; C.R. Thomas, None.
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A Pilot Study to Improve Workflow in an Academic Radiation Oncology Department
R. K. Hales, M. L. Richardson, B. Hristov, R. Drew, T. Yahner, R. Demski, D. Nyberg, T. L. DeWeese Johns Hopkins University, Baltimore, MD Purpose/Objective(s): The delivery of timely, effective and accurate therapy is requisite to maximizing tumor control in patients whose treatments include radiation therapy. Given the complex process associated with radiation treatment planning, systemsbased approaches are needed to minimize delays in the initiation of radiation treatment. Materials/Methods: From December 2007 to September 2008, all patients treated at the Johns Hopkins Department of Radiation Oncology were prospectively coded to indicate whether radiation was initiated on a planned start date assigned at the time of simulation. Additionally, delayed patients were coded to categorize the reason for the delay. An intervention program was then piloted with three provider teams between December 2008 and March 2009 to reduce the incidence of physician-associated factors in treatment delays. The intervention plan included: (1) the initiation of a requisite weekly provider team scheduling conference, (2) reserved slots per provider for simulation, and (3) provider team accountability. Intervention data was prospectively gathered and statistical comparisons were performed using chi-square analysis. Results: Over a 10-month period, 1,652 patients were treated with radiation and the initiation of radiation therapy was delayed in 491 patients (29.7%). Reasons for delays in the initiation of therapy were attributed to: (1) physician delay in the submission of treatment contours to dosimetry, 9.9% (or 33.3% of delayed patients), (2) physician request for patient delay, 7.8%, and (3) non-physician associated delays (i.e., physics/dosimetry initiated delays, patient requests for delay), 12.0%. After the initiation of a pilot intervention program with three provider teams over a 3-month period (n = 113 patients), the average number of patient delays in the intervention group decreased significantly from 29.7% to 15.1% (p = .00086). Patient delays in the intervention period attributable to physician-associated factors were significantly reduced (contour submission delays, 1.8% vs. 9.9%; p = 0.0042; physician request, 0% vs. 7.8%; p = 0.002), but delays associated with other causes were not significantly changed (13.3% vs. 12.0%; p = 0.699). Conclusions: Our data demonstrate that several factors contribute to delays in the initiation of radiation therapy at a major academic radiation department. Furthermore, a pilot program focused on decreasing physician associated delays helped to significantly improve the timeliness of radiation therapy initiation. Taken together, these results show that systems-based approaches can help solve a vexing problem in oncologic care by facilitating team-centered solutions that empower the physician provider. Further goals include the implementation of the interventions to all provider teams in the department. Author Disclosure: R.K. Hales, None; M.L. Richardson, None; B. Hristov, None; R. Drew, None; T. Yahner, None; R. Demski, None; D. Nyberg, None; T.L. DeWeese, None.
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Artificial Neural Network in Predicting Clinical Outcome in NSCLC
J. Kazmierska, T. Piotrowski, J. Malicki Greater Poland Cancer Center, Poznan, Poland Introduction: Comprehensive studies assessing all potential prognostic factors (PF) are often missing and, on the other hand, huge amounts of available scientific data pose a challenge to extract useful and related elements. A neural network can be used to find complex relationships between data and can learn complex relations between input and output e.g., predict an outcome in an individual based on PF published in different studies. Purpose/Objective(s): To assess usefulness of neural network (NN) in searching for relevant PF for overall survival (OS) in advanced NSCLC using a self-learning automated database. Materials/Methods: The first step was the manual creation of a database consisting of the data extracted from relevant studies published between 2006 and 2008 and exploring different OS prognostic factors such as EGFR, KRAS, HER, ERCC status, SUV level, and dose of radiation, etc., for patients with inoperable NSCLC, treated by chemotherapy, radiotherapy and targeted therapy. As an endpoint, we used OS. To create the model, a feed forward multilayer perceptron neural network was selected. The NN inputs consisted of prognostic factors extracted from selected studies, demographic, and clinical factors. As outputs, we used values of OS as achieved in selected studies. Outputs were converted into binary system. The OS \3 months was noted as 0, and .3 months as 1. Such procedure improves performance of NN and allows avoiding local minima.
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1029
A Pilot Study to Improve Workflow in an Academic Radiation Oncology Department
R. K. Hales, M. L. Richardson, B. Hristov, R. Drew, T. Yahner, R. Demski, D. Nyberg, T. L. DeWeese Johns Hopkins University, Baltimore, MD Purpose/Objective(s): The delivery of timely, effective and accurate therapy is requisite to maximizing tumor control in patients whose treatments include radiation therapy. Given the complex process associated with radiation treatment planning, systemsbased approaches are needed to minimize delays in the initiation of radiation treatment. Materials/Methods: From December 2007 to September 2008, all patients treated at the Johns Hopkins Department of Radiation Oncology were prospectively coded to indicate whether radiation was initiated on a planned start date assigned at the time of simulation. Additionally, delayed patients were coded to categorize the reason for the delay. An intervention program was then piloted with three provider teams between December 2008 and March 2009 to reduce the incidence of physician-associated factors in treatment delays. The intervention plan included: (1) the initiation of a requisite weekly provider team scheduling conference, (2) reserved slots per provider for simulation, and (3) provider team accountability. Intervention data was prospectively gathered and statistical comparisons were performed using chi-square analysis. Results: Over a 10-month period, 1,652 patients were treated with radiation and the initiation of radiation therapy was delayed in 491 patients (29.7%). Reasons for delays in the initiation of therapy were attributed to: (1) physician delay in the submission of treatment contours to dosimetry, 9.9% (or 33.3% of delayed patients), (2) physician request for patient delay, 7.8%, and (3) non-physician associated delays (i.e., physics/dosimetry initiated delays, patient requests for delay), 12.0%. After the initiation of a pilot intervention program with three provider teams over a 3-month period (n = 113 patients), the average number of patient delays in the intervention group decreased significantly from 29.7% to 15.1% (p = .00086). Patient delays in the intervention period attributable to physician-associated factors were significantly reduced (contour submission delays, 1.8% vs. 9.9%; p = 0.0042; physician request, 0% vs. 7.8%; p = 0.002), but delays associated with other causes were not significantly changed (13.3% vs. 12.0%; p = 0.699). Conclusions: Our data demonstrate that several factors contribute to delays in the initiation of radiation therapy at a major academic radiation department. Furthermore, a pilot program focused on decreasing physician associated delays helped to significantly improve the timeliness of radiation therapy initiation. Taken together, these results show that systems-based approaches can help solve a vexing problem in oncologic care by facilitating team-centered solutions that empower the physician provider. Further goals include the implementation of the interventions to all provider teams in the department. Author Disclosure: R.K. Hales, None; M.L. Richardson, None; B. Hristov, None; R. Drew, None; T. Yahner, None; R. Demski, None; D. Nyberg, None; T.L. DeWeese, None.
1030
Artificial Neural Network in Predicting Clinical Outcome in NSCLC
J. Kazmierska, T. Piotrowski, J. Malicki Greater Poland Cancer Center, Poznan, Poland Introduction: Comprehensive studies assessing all potential prognostic factors (PF) are often missing and, on the other hand, huge amounts of available scientific data pose a challenge to extract useful and related elements. A neural network can be used to find complex relationships between data and can learn complex relations between input and output e.g., predict an outcome in an individual based on PF published in different studies. Purpose/Objective(s): To assess usefulness of neural network (NN) in searching for relevant PF for overall survival (OS) in advanced NSCLC using a self-learning automated database. Materials/Methods: The first step was the manual creation of a database consisting of the data extracted from relevant studies published between 2006 and 2008 and exploring different OS prognostic factors such as EGFR, KRAS, HER, ERCC status, SUV level, and dose of radiation, etc., for patients with inoperable NSCLC, treated by chemotherapy, radiotherapy and targeted therapy. As an endpoint, we used OS. To create the model, a feed forward multilayer perceptron neural network was selected. The NN inputs consisted of prognostic factors extracted from selected studies, demographic, and clinical factors. As outputs, we used values of OS as achieved in selected studies. Outputs were converted into binary system. The OS \3 months was noted as 0, and .3 months as 1. Such procedure improves performance of NN and allows avoiding local minima.
S137