scholarly journals Interfraction Anatomical Variability Can Lead to Significantly Increased Rectal Dose for Patients Undergoing Stereotactic Body Radiotherapy for Prostate Cancer

2016 ◽  
Vol 16 (2) ◽  
pp. 178-187 ◽  
Author(s):  
Michael Wahl ◽  
Martina Descovich ◽  
Erin Shugard ◽  
Dilini Pinnaduwage ◽  
Atchar Sudhyadhom ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Computed Tomography ◽  
At Risk ◽  
Stereotactic Body Radiotherapy ◽  
Organs At Risk ◽  
Anatomical Variation ◽  
High Dose ◽  
Megavoltage Computed Tomography ◽  
Organ At Risk ◽  
The Impact

Stereotactic body radiotherapy for prostate cancer is rapidly growing in popularity. Stereotactic body radiotherapy plans mimic those of high-dose rate brachytherapy, with tight margins and inhomogeneous dose distributions. The impact of interfraction anatomical changes on the dose received by organs at risk under these conditions has not been well documented. To estimate anatomical variation during stereotactic body radiotherapy, 10 patients were identified who received a prostate boost using robotic stereotactic body radiotherapy after completing 25 fractions of pelvic radiotherapy with daily megavoltage computed tomography. Rectal and bladder volumes were delineated on each megavoltage computed tomography, and the stereotactic body radiotherapy boost plan was registered to each megavoltage computed tomography image using a point-based rigid registration with 3 fiducial markers placed in the prostate. The volume of rectum and bladder receiving 75% of the prescription dose (V75%) was measured for each megavoltage computed tomography. The rectal V75% from the daily megavoltage computed tomographies was significantly greater than the planned V75% (median increase of 0.93 cm3, P < .001), whereas the bladder V75% on megavoltage computed tomography was not significantly changed (median decrease of −0.12 cm3, P = .57). Although daily prostate rotation was significantly correlated with bladder V75% (Spearman ρ = .21, P = .023), there was no association between rotation and rectal V75% or between prostate deformation and either rectal or bladder V75%. Planning organ-at-risk volume-based replanning techniques using either a 6-mm isotropic expansion of the plan rectal contour or a 1-cm expansion from the planning target volume in the superior and posterior directions demonstrated significantly improved rectal V75% on daily megavoltage computed tomographies compared to the original stereotactic body radiotherapy plan, without compromising plan quality. Thus, despite tight margins and full translational and rotational corrections provided by robotic stereotactic body radiotherapy, we find that interfraction anatomical variations can lead to a substantial increase in delivered rectal doses during prostate stereotactic body radiotherapy. A planning organ-at-risk volume-based approach to treatment planning may help mitigate the impact of daily organ motion and reduce the risk of rectal toxicity.

scholarly journals Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

10.2196/26151 ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. e26151
Author(s):  
Stanislav Nikolov ◽  
Sam Blackwell ◽  
Alexei Zverovitch ◽  
Ruheena Mendes ◽  
Michelle Livne ◽  
...  
Keyword(s):  
Computed Tomography ◽  
At Risk ◽  
Deep Learning ◽  
Clinical Practice ◽  
Head And Neck ◽  
Organs At Risk ◽  
Data Set ◽  
Computed Tomography Scans ◽  
Neck Anatomy ◽  
Organ At Risk

Background Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. Objective Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. Methods The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. Results We demonstrated the model’s clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model’s generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. Conclusions Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

The impact of varying rectal contours on rectal dose in prostate cancer patients treated with hypofractionated radiation

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 15598-15598
Author(s):  
B. B. Joshua ◽  
S. Faria ◽  
H. Patrocinio ◽  
F. DeBlois ◽  
M. Duclos ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
At Risk ◽  
Radiation Treatment ◽  
Rectal Wall ◽  
Organs At Risk ◽  
Anal Verge ◽  
Rectal Toxicity ◽  
Rectal Dose ◽  
Organ At Risk ◽  
Definition Of

15598 Background: In curative radiation treatment of prostate cancer,the advent of 3DCRT has made a reduction in the incidence of normal tissue toxicities while optimizing tumor control. To optimize 3DCRT, the ICRU has published standard definitions of target volumes and organs at risk such that the tumor can receive the optimal dose with as little as possible dose to the organs at risk. However, the definition of the rectum as an organ at risk in radiation treatment of the prostate varies widely among institutions and so does the report of toxicities. We studied the effect of varying rectal contouring on rectal dose obtained from DVHs in a homogenous group of prostate cancer patients treated with hypo fractionationed radiation. Methods: 71 patients with favorable risk prostate cancer treated with a total of 66Gy in 3Gy/day fractionation.18 MV photons in a 5-field technique was used. None of the patients received hormonal therapy. Their treatment plans were archived and the rectum was re-contoured by a single investigator. 4 different contours were drawn to compare the rectal dose: i) the whole rectum from the anal verge to the recto sigmoid junction (WR); ii) the rectum from 1cm below the PTV to 1cm above (RPTV); iii) the rectal wall (i.e. the inner and outer rectal wall) from the anal verge to the recto sigmoid junction (RW); iv) the rectal wall from 1cm below the PTV to 1cm above (RWPTV) Results: There were significant differences in the median volume, minimum, mean rectal doses and dose to 50% of the volume, (p=0.0001). The whole rectum (WR) is having the lowest and the rectal wall with 1cm above and below the PTV (RWPTV) having the highest in all the parameters. The only parameter not significantly different among the 4 contours is the maximum rectal dose. Conclusion: the varied rectal contouring across different institutions is a possible reason for the broadly different reports of rectal toxicity after prostate irradiation. Our results suggest significant differences in rectal doses with varied contouring. Contouring the rectal wall only and limiting the volume to 1cm above and below the PTV confers the highest mean rectal dose. Comparison of rectal toxicity between institutions can only be meaningful if a consensual volume definition of the organ at risk is agreed upon. No significant financial relationships to disclose.

A study on planning organ at risk volume for the rectum using cone beam computed tomography in the treatment of prostate cancer

2014 ◽  
Vol 39 (1) ◽  
pp. 38-43 ◽  
Author(s):  
Ramachandran Prabhakar ◽  
Richard Oates ◽  
Daryl Jones ◽  
Tomas Kron ◽  
Jim Cramb ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Computed Tomography ◽  
At Risk ◽  
Cone Beam Computed Tomography ◽  
Cone Beam ◽  
Organ At Risk

scholarly journals Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study (Preprint)

2020 ◽  
Author(s):  
Stanislav Nikolov ◽  
Sam Blackwell ◽  
Alexei Zverovitch ◽  
Ruheena Mendes ◽  
Michelle Livne ◽  
...  
Keyword(s):  
Computed Tomography ◽  
At Risk ◽  
Deep Learning ◽  
Clinical Practice ◽  
Head And Neck ◽  
Organs At Risk ◽  
Data Set ◽  
Computed Tomography Scans ◽  
Neck Anatomy ◽  
Organ At Risk

BACKGROUND Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. OBJECTIVE Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. METHODS The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. RESULTS We demonstrated the model’s clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model’s generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. CONCLUSIONS Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

A comparative dosimetric analysis of virtual stereotactic body radiotherapy to high-dose-rate monotherapy for intermediate-risk prostate cancer

Brachytherapy ◽  
2013 ◽  
Vol 12 (5) ◽  
pp. 428-433 ◽  
Author(s):  
Daniel E. Spratt ◽  
Lawrence M. Scala ◽  
Michael Folkert ◽  
Laszlo Voros ◽  
Gil’ad N. Cohen ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Dose Rate ◽  
Stereotactic Body Radiotherapy ◽  
High Dose Rate ◽  
High Dose ◽  
Intermediate Risk ◽  
Risk Prostate Cancer ◽  
Dosimetric Analysis
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