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A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study

1
Docrates Cancer Center, Saukonpaadenranta 2, FI-00180 Helsinki, Finland
2
MVision Ai, c/o Terkko Health hub, Haartmaninkatu 4, FI-00290 Helsinki, Finland
3
Department of Biostatistics, University of Turku, Kiinamyllynkatu 10, FI-20014 Turku, Finland
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Kuopio University Hospital, Center of Oncology, Kelkkailijantie 7, FI-70210 Kuopio, Finland
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Oulu University Hospital, Department of Oncology and Radiotherapy, Kajaanintie 50, FI-90220 Oulu, Finland
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University of Oulu, Research Unit of Medical Imaging, Physics and Technology, Aapistie 5 A, FI-90220 Oulu, Finland
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North Estonia Medical Centre, J. Sütiste tee 19, 13419 Tallinn, Estonia
8
National Cancer Centre Singapore, Division of Radiation Oncology, 11 Hospital Crescent, Singapore 169610, Singapore
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Oncology Academic Programme, Duke-NUS Medical School, Singapore 169857, Singapore
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National Cancer Centre Singapore, Division of Medical Sciences, Singapore 169610, Singapore
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Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland
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Turku University Hospital, Department of Medical Physics, Hämeentie 11, FI-20521 Turku, Finland
*
Author to whom correspondence should be addressed.
Diagnostics 2020, 10(11), 959; https://doi.org/10.3390/diagnostics10110959
Received: 23 September 2020 / Revised: 6 November 2020 / Accepted: 13 November 2020 / Published: 17 November 2020
(This article belongs to the Special Issue Role of Imaging and Artificial Intelligence in Prostate Cancer)
A commercial deep learning (DL)-based automated segmentation tool (AST) for computed tomography (CT) is evaluated for accuracy and efficiency gain within prostate cancer patients. Thirty patients from six clinics were reviewed with manual- (MC), automated- (AC) and automated and edited (AEC) contouring methods. In the AEC group, created contours (prostate, seminal vesicles, bladder, rectum, femoral heads and penile bulb) were edited, whereas the MC group included empty datasets for MC. In one clinic, lymph node CTV delineations were evaluated for interobserver variability. Compared to MC, the mean time saved using the AST was 12 min for the whole data set (46%) and 12 min for the lymph node CTV (60%), respectively. The delineation consistency between MC and AEC groups according to the Dice similarity coefficient (DSC) improved from 0.78 to 0.94 for the whole data set and from 0.76 to 0.91 for the lymph nodes. The mean DSCs between MC and AC for all six clinics were 0.82 for prostate, 0.72 for seminal vesicles, 0.93 for bladder, 0.84 for rectum, 0.69 for femoral heads and 0.51 for penile bulb. This study proves that using a general DL-based AST for CT images saves time and improves consistency. View Full-Text
Keywords: deep learning; prostate cancer; radiation therapy; autosegmentation; treatment planning deep learning; prostate cancer; radiation therapy; autosegmentation; treatment planning
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MDPI and ACS Style

Kiljunen, T.; Akram, S.; Niemelä, J.; Löyttyniemi, E.; Seppälä, J.; Heikkilä, J.; Vuolukka, K.; Kääriäinen, O.-S.; Heikkilä, V.-P.; Lehtiö, K.; Nikkinen, J.; Gershkevitsh, E.; Borkvel, A.; Adamson, M.; Zolotuhhin, D.; Kolk, K.; Pang, E.P.P.; Tuan, J.K.L.; Master, Z.; Chua, M.L.K.; Joensuu, T.; Kononen, J.; Myllykangas, M.; Riener, M.; Mokka, M.; Keyriläinen, J. A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study. Diagnostics 2020, 10, 959. https://doi.org/10.3390/diagnostics10110959

AMA Style

Kiljunen T, Akram S, Niemelä J, Löyttyniemi E, Seppälä J, Heikkilä J, Vuolukka K, Kääriäinen O-S, Heikkilä V-P, Lehtiö K, Nikkinen J, Gershkevitsh E, Borkvel A, Adamson M, Zolotuhhin D, Kolk K, Pang EPP, Tuan JKL, Master Z, Chua MLK, Joensuu T, Kononen J, Myllykangas M, Riener M, Mokka M, Keyriläinen J. A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study. Diagnostics. 2020; 10(11):959. https://doi.org/10.3390/diagnostics10110959

Chicago/Turabian Style

Kiljunen, Timo; Akram, Saad; Niemelä, Jarkko; Löyttyniemi, Eliisa; Seppälä, Jan; Heikkilä, Janne; Vuolukka, Kristiina; Kääriäinen, Okko-Sakari; Heikkilä, Vesa-Pekka; Lehtiö, Kaisa; Nikkinen, Juha; Gershkevitsh, Eduard; Borkvel, Anni; Adamson, Merve; Zolotuhhin, Daniil; Kolk, Kati; Pang, Eric P.P.; Tuan, Jeffrey K.L.; Master, Zubin; Chua, Melvin L.K.; Joensuu, Timo; Kononen, Juha; Myllykangas, Mikko; Riener, Maigo; Mokka, Miia; Keyriläinen, Jani. 2020. "A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study" Diagnostics 10, no. 11: 959. https://doi.org/10.3390/diagnostics10110959

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