Adaptive planning and uncertainty resolution

Abstract Background Daily anatomical deviations may distort the dose distribution in carbon ion radiotherapy (CIRT), which may cause treatment failure. Therefore, this study aimed to perform re-planning to maintain the dose coverage in patients with pancreatic cancer with passive scattering CIRT. Methods Eight patients with pancreatic cancer and 95 daily computed tomography (CT) sets were examined. Two types of adaptive plans based on new range compensators (RCs) (AP-1) and initial RCs (AP-2) were generated. In AP-2, each beam was optimized by manually adjusting the range shifter thickness and spread-out Bragg peak size to make dose reduction by < 3% of the original plan. Doses of the original plan with bone matching (BM) and tumor matching (TM) were examined for comparison. We calculated the accumulated dose using the contour and intensity-based deformable image registration algorithm. The dosimetric differences in respect to the original plan were compared between methods. Results Using TM and BM, mean ± standard deviations of daily CTV V95 (%) difference from the original plan was − 5.1 ± 6.2 and − 8.8 ± 8.8, respectively, but 1.2 ± 3.4 in AP-1 and − 0.5 ± 2.1 in AP-2 (P < 0.001). AP-1 and AP-2 enabled to maintain a satisfactory accumulated dose in all patients. The dose difference was 1.2 ± 2.8, − 2,1 ± 1.7, − 7.1 ± 5.2, and − 16.5 ± 15.0 for AP-1, AP-2, TM, and BM, respectively. However, AP-2 caused a dose increase in the duodenum, especially in the left–right beam. Conclusions The possible dose deterioration should be considered when performing the BM, even TM. Re-planning based on single beam optimization in passive scattering CIRT seems an effective and safe method of ensuring the treatment robustness in pancreatic cancer. Further study is necessary to spare healthy tissues, especially the duodenum.

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Feasibility of Continual Deep Learning-Based Segmentation for Personalized Adaptive Radiation Therapy in Head and Neck Area

Cancers ◽

10.3390/cancers13040702 ◽

2021 ◽

Vol 13 (4) ◽

pp. 702

Author(s):

Nalee Kim ◽

Jaehee Chun ◽

Jee Suk Chang ◽

Chang Geol Lee ◽

Ki Chang Keum ◽

...

Keyword(s):

Deep Learning ◽

Head And Neck ◽

Organs At Risk ◽

Subjective Assessment ◽

Turing Test ◽

Similar Rate ◽

Dice Similarity Coefficient ◽

Adaptive Planning ◽

Test Set ◽

Using Data

This study investigated the feasibility of deep learning-based segmentation (DLS) and continual training for adaptive radiotherapy (RT) of head and neck (H&N) cancer. One-hundred patients treated with definitive RT were included. Based on 23 organs-at-risk (OARs) manually segmented in initial planning computed tomography (CT), modified FC-DenseNet was trained for DLS: (i) using data obtained from 60 patients, with 20 matched patients in the test set (DLSm); (ii) using data obtained from 60 identical patients with 20 unmatched patients in the test set (DLSu). Manually contoured OARs in adaptive planning CT for independent 20 patients were provided as test sets. Deformable image registration (DIR) was also performed. All 23 OARs were compared using quantitative measurements, and nine OARs were also evaluated via subjective assessment from 26 observers using the Turing test. DLSm achieved better performance than both DLSu and DIR (mean Dice similarity coefficient; 0.83 vs. 0.80 vs. 0.70), mainly for glandular structures, whose volume significantly reduced during RT. Based on subjective measurements, DLS is often perceived as a human (49.2%). Furthermore, DLSm is preferred over DLSu (67.2%) and DIR (96.7%), with a similar rate of required revision to that of manual segmentation (28.0% vs. 29.7%). In conclusion, DLS was effective and preferred over DIR. Additionally, continual DLS training is required for an effective optimization and robustness in personalized adaptive RT.

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Hope: An empirical study of attitude toward the timing of uncertainty resolution

Journal of Risk and Uncertainty ◽

10.1007/bf01064045 ◽

1994 ◽

Vol 8 (3) ◽

pp. 267-288 ◽

Cited By ~ 56

Author(s):

Soo Hong Chew ◽

Joanna L. Ho

Keyword(s):

Empirical Study ◽

Uncertainty Resolution ◽

Timing Of Uncertainty Resolution

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Adaptive Planning: The Role of Institution-Specific Models

The Journal of Higher Education ◽

10.2307/1981227 ◽

1979 ◽

Vol 50 (5) ◽

pp. 586 ◽

Cited By ~ 1

Author(s):

Randall Porter ◽

Robert Zemsky ◽

Penney Oedel

Keyword(s):

Adaptive Planning

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Losing time for the tiger Panthera tigris: delayed action puts a globally threatened species at risk of local extinction

Oryx ◽

10.1017/s0030605317001156 ◽

2017 ◽

Vol 52 (1) ◽

pp. 78-88 ◽

Cited By ~ 4

Author(s):

Abishek Harihar ◽

Mousumi Ghosh-Harihar ◽

Douglas C. MacMillan

Keyword(s):

Decision Making ◽

Local Extinction ◽

Decision Making Process ◽

Western India ◽

Ecological Knowledge ◽

Panthera Tigris ◽

Adaptive Planning ◽

Conservation Action ◽

Project Appraisal ◽

Policy Agendas

AbstractMeeting global and regional environmental targets is challenging, given the multiplicity of stakeholders and their diverse and often competing policy agendas and objectives. Relatively few studies have sought to systematically analyse the progress, or lack thereof, of institutionally complex and diffuse projects. Here we analyse one such project, which aims to protect and restore a critical landscape corridor for tigers Panthera tigris in north-western India, using a temporal–analytic framework that integrates ecological information on species population status and spatial connectivity modelling with a systematic examination of the decision-making process. We find that even with adequate ecological knowledge the tiger population is on the verge of local extinction because of weak institutional support, poor adaptive planning and ineffective leadership in a complex political arena, which has led to delays in conservation action. From the outset the conservation agencies and NGOs that were the primary drivers of the project lacked awareness of the political idiosyncrasies of coordinating the actions of disparate agencies within the decision-making process. To secure better future environmental outcomes we recommend the adoption of an improved project appraisal methodology that explicitly encompasses an evaluation of organizational incentives, to determine political buy-in, including alignment with organizational objectives and funding availability.

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