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Automatic contouring system for cervical cancer using convolutional neural networks.

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Title: Automatic contouring system for cervical cancer using convolutional neural networks.
Authors: Rhee, Dong Joo1,2 (AUTHOR) drhee1@mdanderson.org, Jhingran, Anuja3 (AUTHOR), Rigaud, Bastien4 (AUTHOR), Netherton, Tucker1,2 (AUTHOR), Cardenas, Carlos E.2 (AUTHOR), Zhang, Lifei2 (AUTHOR), Vedam, Sastry2 (AUTHOR), Kry, Stephen2 (AUTHOR), Brock, Kristy K.4 (AUTHOR), Shaw, William5 (AUTHOR), O'Reilly, Frederika5 (AUTHOR), Parkes, Jeannette6 (AUTHOR), Burger, Hester6 (AUTHOR), Fakie, Nazia6 (AUTHOR), Trauernicht, Chris7 (AUTHOR), Simonds, Hannah8 (AUTHOR), Court, Laurence E.2 (AUTHOR)
Source: Medical Physics. Nov2020, Vol. 47 Issue 11, p5648-5658. 11p.
Subjects: Convolutional neural networks, Cervical cancer, Radiotherapy treatment planning, Spinal cord, Pelvic bones
Abstract: Purpose: To develop a tool for the automatic contouring of clinical treatment volumes (CTVs) and normal tissues for radiotherapy treatment planning in cervical cancer patients. Methods: An auto‐contouring tool based on convolutional neural networks (CNN) was developed to delineate three cervical CTVs and 11 normal structures (seven OARs, four bony structures) in cervical cancer treatment for use with the Radiation Planning Assistant, a web‐based automatic plan generation system. A total of 2254 retrospective clinical computed tomography (CT) scans from a single cancer center and 210 CT scans from a segmentation challenge were used to train and validate the CNN‐based auto‐contouring tool. The accuracy of the tool was evaluated by calculating the Sørensen‐dice similarity coefficient (DSC) and mean surface and Hausdorff distances between the automatically generated contours and physician‐drawn contours on 140 internal CT scans. A radiation oncologist scored the automatically generated contours on 30 external CT scans from three South African hospitals. Results: The average DSC, mean surface distance, and Hausdorff distance of our CNN‐based tool were 0.86/0.19 cm/2.02 cm for the primary CTV, 0.81/0.21 cm/2.09 cm for the nodal CTV, 0.76/0.27 cm/2.00 cm for the PAN CTV, 0.89/0.11 cm/1.07 cm for the bladder, 0.81/0.18 cm/1.66 cm for the rectum, 0.90/0.06 cm/0.65 cm for the spinal cord, 0.94/0.06 cm/0.60 cm for the left femur, 0.93/0.07 cm/0.66 cm for the right femur, 0.94/0.08 cm/0.76 cm for the left kidney, 0.95/0.07 cm/0.84 cm for the right kidney, 0.93/0.05 cm/1.06 cm for the pelvic bone, 0.91/0.07 cm/1.25 cm for the sacrum, 0.91/0.07 cm/0.53 cm for the L4 vertebral body, and 0.90/0.08 cm/0.68 cm for the L5 vertebral bodies. On average, 80% of the CTVs, 97% of the organ at risk, and 98% of the bony structure contours in the external test dataset were clinically acceptable based on physician review. Conclusions: Our CNN‐based auto‐contouring tool performed well on both internal and external datasets and had a high rate of clinical acceptability. [ABSTRACT FROM AUTHOR]
Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Automatic contouring system for cervical cancer using convolutional neural networks.
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  Data: <searchLink fieldCode="AR" term="%22Rhee%2C+Dong+Joo%22">Rhee, Dong Joo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> drhee1@mdanderson.org</i><br /><searchLink fieldCode="AR" term="%22Jhingran%2C+Anuja%22">Jhingran, Anuja</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rigaud%2C+Bastien%22">Rigaud, Bastien</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Netherton%2C+Tucker%22">Netherton, Tucker</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cardenas%2C+Carlos+E%2E%22">Cardenas, Carlos E.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Lifei%22">Zhang, Lifei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Vedam%2C+Sastry%22">Vedam, Sastry</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kry%2C+Stephen%22">Kry, Stephen</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Brock%2C+Kristy+K%2E%22">Brock, Kristy K.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shaw%2C+William%22">Shaw, William</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22O'Reilly%2C+Frederika%22">O'Reilly, Frederika</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Parkes%2C+Jeannette%22">Parkes, Jeannette</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Burger%2C+Hester%22">Burger, Hester</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fakie%2C+Nazia%22">Fakie, Nazia</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Trauernicht%2C+Chris%22">Trauernicht, Chris</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Simonds%2C+Hannah%22">Simonds, Hannah</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Court%2C+Laurence+E%2E%22">Court, Laurence E.</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Nov2020, Vol. 47 Issue 11, p5648-5658. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Cervical+cancer%22">Cervical cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Radiotherapy+treatment+planning%22">Radiotherapy treatment planning</searchLink><br /><searchLink fieldCode="DE" term="%22Spinal+cord%22">Spinal cord</searchLink><br /><searchLink fieldCode="DE" term="%22Pelvic+bones%22">Pelvic bones</searchLink>
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  Label: Abstract
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  Data: Purpose: To develop a tool for the automatic contouring of clinical treatment volumes (CTVs) and normal tissues for radiotherapy treatment planning in cervical cancer patients. Methods: An auto‐contouring tool based on convolutional neural networks (CNN) was developed to delineate three cervical CTVs and 11 normal structures (seven OARs, four bony structures) in cervical cancer treatment for use with the Radiation Planning Assistant, a web‐based automatic plan generation system. A total of 2254 retrospective clinical computed tomography (CT) scans from a single cancer center and 210 CT scans from a segmentation challenge were used to train and validate the CNN‐based auto‐contouring tool. The accuracy of the tool was evaluated by calculating the Sørensen‐dice similarity coefficient (DSC) and mean surface and Hausdorff distances between the automatically generated contours and physician‐drawn contours on 140 internal CT scans. A radiation oncologist scored the automatically generated contours on 30 external CT scans from three South African hospitals. Results: The average DSC, mean surface distance, and Hausdorff distance of our CNN‐based tool were 0.86/0.19 cm/2.02 cm for the primary CTV, 0.81/0.21 cm/2.09 cm for the nodal CTV, 0.76/0.27 cm/2.00 cm for the PAN CTV, 0.89/0.11 cm/1.07 cm for the bladder, 0.81/0.18 cm/1.66 cm for the rectum, 0.90/0.06 cm/0.65 cm for the spinal cord, 0.94/0.06 cm/0.60 cm for the left femur, 0.93/0.07 cm/0.66 cm for the right femur, 0.94/0.08 cm/0.76 cm for the left kidney, 0.95/0.07 cm/0.84 cm for the right kidney, 0.93/0.05 cm/1.06 cm for the pelvic bone, 0.91/0.07 cm/1.25 cm for the sacrum, 0.91/0.07 cm/0.53 cm for the L4 vertebral body, and 0.90/0.08 cm/0.68 cm for the L5 vertebral bodies. On average, 80% of the CTVs, 97% of the organ at risk, and 98% of the bony structure contours in the external test dataset were clinically acceptable based on physician review. Conclusions: Our CNN‐based auto‐contouring tool performed well on both internal and external datasets and had a high rate of clinical acceptability. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/mp.14467
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        Text: English
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      – SubjectFull: Convolutional neural networks
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      – SubjectFull: Cervical cancer
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      – SubjectFull: Radiotherapy treatment planning
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      – SubjectFull: Spinal cord
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      – SubjectFull: Pelvic bones
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