Dong Joo Rhee1,2, Anuja Jhingran3, Bastien Rigaud4, Tucker Netherton1,2, Carlos E Cardenas2, Lifei Zhang2, Sastry Vedam2, Stephen Kry2, Kristy K Brock4, William Shaw5, Frederika O'Reilly5, Jeannette Parkes6, Hester Burger6, Nazia Fakie6, Chris Trauernicht7, Hannah Simonds8, Laurence E Court2. 1. MD Anderson UTHealth Graduate School, Houston, TX, USA. 2. Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 3. Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 4. Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 5. Department of Medical Physics (G68), University of the Free State, Bloemfontein, South Africa. 6. Division of Radiation Oncology and Medical Physics, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa. 7. Division of Medical Physics, Stellenbosch University, Tygerberg Academic Hospital, Cape Town, South Africa. 8. Division of Radiation Oncology, Stellenbosch University, Tygerberg Academic Hospital, Cape Town, South Africa.
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.
PURPOSE: To develop a tool for the automatic contouring of clinical treatment volumes (CTVs) and normal tissues for radiotherapy treatment planning in cervical cancerpatients. 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.
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Authors: Dong Joo Rhee; Anuja Jhingran; Kai Huang; Tucker J Netherton; Nazia Fakie; Ingrid White; Alicia Sherriff; Carlos E Cardenas; Lifei Zhang; Surendra Prajapati; Stephen F Kry; Beth M Beadle; William Shaw; Frederika O'Reilly; Jeannette Parkes; Hester Burger; Chris Trauernicht; Hannah Simonds; Laurence E Court Journal: Med Phys Date: 2022-07-26 Impact factor: 4.506
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