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Tegtmeier RC, Clouser EL, Laughlin BS, Santos Toesca DA, Flakus MJ, Bashir S, Kutyreff CJ, Hobbis D, Harrington DP, Smetanick JL, Yu NY, Vargas CE, James SE, Rwigema JCM, Rong Y. Evaluation of knowledge-based planning models for male pelvic CBCT-based online adaptive radiotherapy on conventional linear accelerators. J Appl Clin Med Phys 2024:e14464. [PMID: 39031902 DOI: 10.1002/acm2.14464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 06/10/2024] [Accepted: 06/28/2024] [Indexed: 07/22/2024] Open
Abstract
PURPOSE To assess the practicality of employing a commercial knowledge-based planning tool (RapidPlan) to generate adapted intact prostate and prostate bed volumetric modulated arc therapy (VMAT) plans on iterative cone-beam computed tomography (iCBCT) datasets. METHODS AND MATERIALS Intact prostate and prostate bed RapidPlan models were trained utilizing planning data from 50 and 44 clinical cases, respectively. To ensure that refined models were capable of producing adequate clinical plans with a single optimization, models were tested with 50 clinical planning CT datasets by comparing dose-volume histogram (DVH) and plan quality metric (PQM) values between clinical and RapidPlan-generated plans. The RapidPlan tool was then used to retrospectively generate adapted VMAT plans on daily iCBCT images for 20 intact prostate and 15 prostate bed cases. As before, DVH and PQM metrics were utilized to dosimetrically compare scheduled (iCBCT Verify) and adapted (iCBCT RapidPlan) plans. Timing data was collected to further evaluate the feasibility of integrating this approach within an online adaptive radiotherapy workflow. RESULTS Model testing results confirmed the models were capable of producing VMAT plans within a single optimization that were overall improved upon or dosimetrically comparable to original clinical plans. Direct application of RapidPlan on iCBCT datasets produced satisfactory intact prostate and prostate bed plans with generally improved target volume coverage/conformality and rectal sparing relative to iCBCT Verify plans as indicated by DVH values, though bladder metrics were marginally increased on average. Average PQM values for iCBCT RapidPlans were significantly improved compared to iCBCT Verify plans. The average time required [in mm:ss] to generate adapted plans was 06:09 ± 02:06 (intact) and 07:12 ± 01:04 (bed). CONCLUSION This study demonstrated the feasibility of leveraging RapidPlan to expeditiously generate adapted VMAT intact prostate and prostate bed plans on iCBCT datasets. In general, adapted plans were dosimetrically improved relative to scheduled plans, emphasizing the practicality of the proposed approach.
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Affiliation(s)
- Riley C Tegtmeier
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Edward L Clouser
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Brady S Laughlin
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | | | - Mattison J Flakus
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Sara Bashir
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | | | - Dean Hobbis
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
- Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Daniel P Harrington
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | | | - Nathan Y Yu
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Carlos E Vargas
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Sarah E James
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | | | - Yi Rong
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
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Cavus H, Rondagh T, Jankelevitch A, Tournel K, Orlandini M, Bulens P, Delombaerde L, Geens K, Crijns W, Reniers B. Optimizing volumetric modulated arc therapy prostate planning using an automated Fine-Tuning process through dynamic adjustment of optimization parameters. Phys Imaging Radiat Oncol 2024; 31:100619. [PMID: 39220116 PMCID: PMC11363496 DOI: 10.1016/j.phro.2024.100619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 07/29/2024] [Accepted: 07/30/2024] [Indexed: 09/04/2024] Open
Abstract
In radiotherapy treatment planning, optimization is essential for achieving the most favorable plan by adjusting optimization criteria. This study introduced an innovative approach to automatically fine-tune optimization parameters for volumetric modulated arc therapy prostate planning, ensuring all constraints were met. A knowledge-based planning model was invoked, and the fine-tuning process was applied through an in-house developed script. Among 25 prostate plans, this fine-tuning increased the number of plans meeting all constraints from 10/25 to 22/25, with a reduction in mean monitor units per gray without increasing plan's complexity. This automation improved efficiency by saving time and resources in treatment planning.
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Affiliation(s)
- Hasan Cavus
- Department of Radiation Oncology, Jessa Hospital, 3500 Hasselt, Belgium
- Limburg Oncology Center, 3500 Hasselt, Belgium
- Faculty of Engineering Technology, Hasselt University, B- 3590, Diepenbeek, Belgium
| | - Thierry Rondagh
- Department of Radiation Oncology, Jessa Hospital, 3500 Hasselt, Belgium
- Limburg Oncology Center, 3500 Hasselt, Belgium
| | - Alexandra Jankelevitch
- Department of Radiation Oncology, Jessa Hospital, 3500 Hasselt, Belgium
- Limburg Oncology Center, 3500 Hasselt, Belgium
| | - Koen Tournel
- Department of Radiation Oncology, Jessa Hospital, 3500 Hasselt, Belgium
- Limburg Oncology Center, 3500 Hasselt, Belgium
| | - Marc Orlandini
- Department of Radiation Oncology, Jessa Hospital, 3500 Hasselt, Belgium
- Limburg Oncology Center, 3500 Hasselt, Belgium
| | - Philippe Bulens
- Department of Radiation Oncology, Jessa Hospital, 3500 Hasselt, Belgium
- Limburg Oncology Center, 3500 Hasselt, Belgium
| | - Laurence Delombaerde
- Department Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium
| | - Kenny Geens
- Department of Radiation Oncology, Jessa Hospital, 3500 Hasselt, Belgium
- Limburg Oncology Center, 3500 Hasselt, Belgium
| | - Wouter Crijns
- Department Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium
- Department of Radiation Oncology, UZ Leuven, Belgium
| | - Brigitte Reniers
- Faculty of Engineering Technology, Hasselt University, B- 3590, Diepenbeek, Belgium
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3
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Masumoto N, Sasaki M, Nakaguchi Y, Kamomae T, Kanazawa Y, Ikushima H. Knowledge-based model building for treatment planning for prostate cancer using commercial treatment planning quality assurance software tools. Radiol Phys Technol 2024; 17:337-345. [PMID: 37938420 DOI: 10.1007/s12194-023-00759-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 10/19/2023] [Accepted: 10/20/2023] [Indexed: 11/09/2023]
Abstract
This study devised a method to efficiently launch the RapidPlan model for volumetric-modulated arc therapy for prostate cancer in small- and medium-sized facilities using high-quality treatment plans with the PlanIQ software as a reference. Treatment plans were generated for 30 patients with prostate cancer to construct the RapidPlan model using PlanIQ as a reference. In the context of PlanIQ-referenced treatment planning, treatment plans were developed, such that the feasibility dose-volume histogram of each organ-at-risk fell within F ≤ 0.1. For validation of the RapidPlan model, treatment plans were formulated for 20 patients using both RapidPlan and PlanIQ, and the differences were evaluated. The results of RapidPlan model validity assessment revealed that the RapidPlan-produced treatment plans exhibited higher quality in 11 of 20 patients. No significant differences were found between the treatment plans. In conclusion, high-quality treatment plans formulated using PlanIQ as reference facilitated efficient implementation of RapidPlan modeling.
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Affiliation(s)
- Nagi Masumoto
- Medical Research Institute Kitano Hospital, PIIF Tazuke-kofukai, Osaka, Osaka, 530-8480, Japan
| | - Motoharu Sasaki
- Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, Tokushima, 770-8503, Japan.
| | - Yuji Nakaguchi
- Toyo Medic Co., Ltd, Shinjyukuku, Tokyo, 162-0813, Japan
| | - Takeshi Kamomae
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, 466-8550, Japan
| | - Yuki Kanazawa
- Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, Tokushima, 770-8503, Japan
| | - Hitoshi Ikushima
- Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, Tokushima, 770-8503, Japan
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Murakami Y, Kawahara D, Soyano T, Kozuka T, Takahashi Y, Miyake K, Kashihara K, Kashihara T, Kamima T, Oguchi M, Murakami Y, Yoshioka Y, Nagata Y. Dosiomics for intensity-modulated radiotherapy in patients with prostate cancer: survival analysis stratified by baseline prostate-specific antigen and Gleason grade group in a 2-institutional retrospective study. Br J Radiol 2024; 97:142-149. [PMID: 38263831 PMCID: PMC11008500 DOI: 10.1093/bjr/tqad004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 05/25/2023] [Accepted: 10/12/2023] [Indexed: 01/25/2024] Open
Abstract
OBJECTIVE This study evaluated the prognostic impact of the quality of dose distribution using dosiomics in patients with prostate cancer, stratified by pretreatment prostate-specific antigen (PSA) levels and Gleason grade (GG) group. METHODS A total of 721 patients (Japanese Foundation for Cancer Research [JFCR] cohort: N = 489 and Tokyo Radiation Oncology Clinic [TROC] cohort: N = 232) with localized prostate cancer treated by intensity-modulated radiation therapy were enrolled. Two predictive dosiomic features for biochemical recurrence (BCR) were selected and patients were divided into certain groups stratified by pretreatment PSA levels and GG. Freedom from biochemical failure (FFBF) was estimated using the Kaplan-Meier method based on each dosiomic feature and univariate discrimination was evaluated using the log-rank test. As an exploratory analysis, a dosiomics hazard (DH) score was developed and its prognostic power for BCR was examined. RESULTS The dosiomic feature extracted from planning target volume (PTV) significantly distinguished the high- and low-risk groups in patients with PSA levels >10 ng/mL (7-year FFBF: 86.7% vs 76.1%, P < .01), GG 4 (92.2% vs 76.9%, P < .01), and GG 5 (83.1% vs 77.8%, P = .04). The DH score showed significant association with BCR (hazard score: 2.04; 95% confidence interval: 1.38-3.01; P < .001). CONCLUSION The quality of planned dose distribution on PTV may affect the prognosis of patients with poor prognostic factors, such as PSA levels >10 ng/mL and higher GGs. ADVANCES IN KNOWLEDGE The effects of planned dose distribution on prognosis differ depending on the patient's clinical background.
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Affiliation(s)
- Yu Murakami
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University,1-2-3 Kasumi, Hiroshima, 734-8551, Japan
- Department of Physics, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
| | - Daisuke Kawahara
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University,1-2-3 Kasumi, Hiroshima, 734-8551, Japan
| | - Takashi Soyano
- Department of Radiology, Japan Self-Defense Forces Central Hospital, 1-2-24 Ikejiri, Setagaya-ku, Tokyo 154-8532, Japan
| | - Takuyo Kozuka
- Department of Radiology, University of Tokyo Hospital, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Yuka Takahashi
- Tokyo Radiation Oncology Clinic, 3-5-7, Ariake, Koto-ku, Tokyo 135-0063, Japan
| | - Konatsu Miyake
- Tokyo Radiation Oncology Clinic, 3-5-7, Ariake, Koto-ku, Tokyo 135-0063, Japan
| | - Kenichi Kashihara
- Tokyo Radiation Oncology Clinic, 3-5-7, Ariake, Koto-ku, Tokyo 135-0063, Japan
| | - Tairo Kashihara
- Department of Radiation Oncology, National Cancer Center Hospital, 5-1-1, Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Tatsuya Kamima
- Radiation Oncology Department, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
| | - Masahiko Oguchi
- Radiation Oncology Department, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
| | - Yuji Murakami
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University,1-2-3 Kasumi, Hiroshima, 734-8551, Japan
| | - Yasuo Yoshioka
- Radiation Oncology Department, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
| | - Yasushi Nagata
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University,1-2-3 Kasumi, Hiroshima, 734-8551, Japan
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Kawamura M, Kamomae T, Yanagawa M, Kamagata K, Fujita S, Ueda D, Matsui Y, Fushimi Y, Fujioka T, Nozaki T, Yamada A, Hirata K, Ito R, Fujima N, Tatsugami F, Nakaura T, Tsuboyama T, Naganawa S. Revolutionizing radiation therapy: the role of AI in clinical practice. JOURNAL OF RADIATION RESEARCH 2024; 65:1-9. [PMID: 37996085 PMCID: PMC10803173 DOI: 10.1093/jrr/rrad090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/25/2023] [Accepted: 10/16/2023] [Indexed: 11/25/2023]
Abstract
This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist's perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.
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Affiliation(s)
- Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Takeshi Kamomae
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Shohei Fujita
- Department of Radiology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kitaku, Okayama, 700-8558, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Faculty of Medicine, Hokkaido University, Kita15, Nishi7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Kita15, Nishi7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, 1-1-1 Honjo, Chuo-ku, Kumamoto, 860-8556, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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Sinha S, Kumar A, Maheshwari G, Mohanty S, Joshi K, Shinde P, Gupta D, Kale S, Phurailatpam R, Swain M, Budrukkar A, Kinhikar R, Ghosh-Laskar S. Development and Validation of Single-Optimization Knowledge-Based Volumetric Modulated Arc Therapy Model Plan in Nasopharyngeal Carcinomas. Adv Radiat Oncol 2024; 9:101311. [PMID: 38260222 PMCID: PMC10801663 DOI: 10.1016/j.adro.2023.101311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 06/27/2023] [Indexed: 01/24/2024] Open
Abstract
Purpose Knowledge-based planning (KBP) has evolved to standardize and expedite the complex process of radiation therapy planning for nasopharyngeal cancer (NPC). Herein, we aim to develop and validate the suitability of a single-optimization KBP for NPC. Methods and Materials Volumetric modulated arc therapy plans of 103 patients with NPC treated between 2016 and 2020 were reviewed and used to generate a KBP model. A validation set of 15 patients was employed to compare the quality of single optimization KBP and clinical plans using the paired t test and the Wilcoxon signed rank test. The time required for either planning was also analyzed. Results Most patients (86.7%) were of locally advanced stage (III/IV). The median dose received by 95% of the high-risk planning target volume was significantly higher for the KBP (97.1% vs 96.4%; P = .017). The median homogeneity (0.09 vs 0.1) and conformity (0.98 vs 0.97) indices for high-risk planning target volume and sparing of the normal tissues like optic structures, spinal cord, and uninvolved dysphagia and aspiration-related structures were better with the KBP (P < .05). In the blinded evaluation, the physician preferred the KBP plan in 13 out of 15 patients. The median time required to generate the KBP and manual plans was 53 and 77 minutes, respectively. Conclusions KBP with a single optimization is an efficient and time saving alternative for manual planning in NPC.
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Affiliation(s)
- Shwetabh Sinha
- Department of Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Anuj Kumar
- Department of Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Guncha Maheshwari
- Department of Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Samarpita Mohanty
- Department of Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Kishore Joshi
- Department of Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Prakash Shinde
- Department of Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Deeksha Gupta
- Department of Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Shrikant Kale
- Department of Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Reena Phurailatpam
- Department of Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Monali Swain
- Department of Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Ashwini Budrukkar
- Department of Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Rajesh Kinhikar
- Department of Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Sarbani Ghosh-Laskar
- Department of Radiation Oncology and Medical Physics, ACTREC/TMH, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
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Ayuthaya IIN, Sanghangthum T, Oonsiri P, Oonsiri S, Keawsamur M, Ruangchan S, Vannavijit C, Kanphet J, Kingkaew S, Vimolnoch M, Tawonwong T, Plangpleng N. Multi-planner validation of RapidPlan knowledge-based model for volumetric modulated arc therapy in prostate cancer. J Appl Clin Med Phys 2024; 25:e14223. [PMID: 38009569 PMCID: PMC10795433 DOI: 10.1002/acm2.14223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 11/01/2023] [Accepted: 11/08/2023] [Indexed: 11/29/2023] Open
Abstract
PURPOSE To investigate the performance of a model-based optimization process for volumetric modulated arc therapy (VMAT) applied to prostate cancer patients with the multi-planner. METHODS AND MATERIALS The 120 prostate plans for VMAT treatment were entered into the database system of the RapidPlan (RP) knowledge-based treatment planning. The treatment planning data for each plan was used to create and train the RP model. Twelve prostate cancer cases were selected and were used for planning by a manual of 12 planners based on the clinical protocol for dose constraints. Then, the treatment plans for each patient were compared with the RP model plans and analyzed with Wilcoxon tests. RESULTS On average, the RP models can estimate comparable doses among all planner plans and clinical plans for the PTV, which Dmax , D95% , D98% , HI, and CI were used to evaluate. For the normal organ doses of the bladder, rectum, penile bulb, and femoral head, all RP model plans showed comparable or better dose sparing than all planner plans and clinical plans. Moreover, the average planning time of the RP model was faster than manual plans by about two times. The RP model can significantly reduce the variation dose of the normal organs compared with the manual plans among the planners. CONCLUSION The automated plans of the RP model might benefit from further fine-tuning of the dose constraints of the normal organs, although both procedure plans are acceptable and fulfill the clinical protocol goals so that the RP model can enhance the efficacy and quality of plans.
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Affiliation(s)
- Isra Israngkul Na Ayuthaya
- Division of Radiation OncologyDepartment of RadiologyKing Chulalongkorn Memorial HospitalBangkokThailand
| | - Taweap Sanghangthum
- Division of Radiation OncologyDepartment of RadiologyFaculty of MedicineChulalongkorn UniversityBangkokThailand
| | - Puntiwa Oonsiri
- Division of Radiation OncologyDepartment of RadiologyKing Chulalongkorn Memorial HospitalBangkokThailand
| | - Sornjarod Oonsiri
- Division of Radiation OncologyDepartment of RadiologyKing Chulalongkorn Memorial HospitalBangkokThailand
| | - Mintra Keawsamur
- Division of Radiation OncologyDepartment of RadiologyKing Chulalongkorn Memorial HospitalBangkokThailand
| | - Sirinya Ruangchan
- Division of Radiation OncologyDepartment of RadiologyKing Chulalongkorn Memorial HospitalBangkokThailand
| | - Chulee Vannavijit
- Division of Radiation OncologyDepartment of RadiologyKing Chulalongkorn Memorial HospitalBangkokThailand
| | - Jaruek Kanphet
- Division of Radiation OncologyDepartment of RadiologyKing Chulalongkorn Memorial HospitalBangkokThailand
| | - Sakda Kingkaew
- Division of Radiation OncologyDepartment of RadiologyKing Chulalongkorn Memorial HospitalBangkokThailand
| | - Mananchaya Vimolnoch
- Division of Radiation OncologyDepartment of RadiologyKing Chulalongkorn Memorial HospitalBangkokThailand
| | - Tanawat Tawonwong
- Division of Radiation OncologyDepartment of RadiologyKing Chulalongkorn Memorial HospitalBangkokThailand
| | - Nuttha Plangpleng
- Division of Radiation OncologyDepartment of RadiologyKing Chulalongkorn Memorial HospitalBangkokThailand
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Fjellanger K, Hordnes M, Sandvik IM, Sulen TH, Heijmen BJM, Breedveld S, Rossi L, Pettersen HES, Hysing LB. Improving knowledge-based treatment planning for lung cancer radiotherapy with automatic multi-criteria optimized training plans. Acta Oncol 2023; 62:1194-1200. [PMID: 37589124 DOI: 10.1080/0284186x.2023.2238882] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 07/04/2023] [Indexed: 08/18/2023]
Abstract
BACKGROUND Knowledge-based planning (KBP) is a method for automated radiotherapy treatment planning where appropriate optimization objectives for new patients are predicted based on a library of training plans. KBP can save time and improve organ at-risk sparing and inter-patient consistency compared to manual planning, but its performance depends on the quality of the training plans. We used another system for automated planning, which generates multi-criteria optimized (MCO) plans based on a wish list, to create training plans for the KBP model, to allow seamless integration of knowledge from a new system into clinical routine. Model performance was compared for KBP models trained with manually created and automatic MCO treatment plans. MATERIAL AND METHODS Two RapidPlan models with the same 30 locally advanced non-small cell lung cancer patients included were created, one containing manually created clinical plans (RP_CLIN) and one containing fully automatic multi-criteria optimized plans (RP_MCO). For 15 validation patients, model performance was compared in terms of dose-volume parameters and normal tissue complication probabilities, and an oncologist performed a blind comparison of the clinical (CLIN), RP_CLIN, and RP_MCO plans. RESULTS The heart and esophagus doses were lower for RP_MCO compared to RP_CLIN, resulting in an average reduction in the risk of 2-year mortality by 0.9 percentage points and the risk of acute esophageal toxicity by 1.6 percentage points with RP_MCO. The oncologist preferred the RP_MCO plan for 8 patients and the CLIN plan for 7 patients, while the RP_CLIN plan was not preferred for any patients. CONCLUSION RP_MCO improved OAR sparing compared to RP_CLIN and was selected for implementation in the clinic. Training a KBP model with clinical plans may lead to suboptimal output plans, and making an extra effort to optimize the library plans in the KBP model creation phase can improve the plan quality for many future patients.
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Affiliation(s)
- Kristine Fjellanger
- Department of Oncology and Medical Physics, Haukeland University Hospital, Bergen, Norway
- Institute of Physics and Technology, University of Bergen, Bergen, Norway
| | - Marte Hordnes
- Institute of Physics and Technology, University of Bergen, Bergen, Norway
| | - Inger Marie Sandvik
- Department of Oncology and Medical Physics, Haukeland University Hospital, Bergen, Norway
| | - Turid Husevåg Sulen
- Department of Oncology and Medical Physics, Haukeland University Hospital, Bergen, Norway
| | - Ben J M Heijmen
- Department of Radiotherapy, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Sebastiaan Breedveld
- Department of Radiotherapy, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Linda Rossi
- Department of Radiotherapy, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, Netherlands
| | | | - Liv Bolstad Hysing
- Department of Oncology and Medical Physics, Haukeland University Hospital, Bergen, Norway
- Institute of Physics and Technology, University of Bergen, Bergen, Norway
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Kadoya N, Kimura Y, Tozuka R, Tanaka S, Arai K, Katsuta Y, Shimizu H, Sugai Y, Yamamoto T, Umezawa R, Jingu K. Evaluation of deep learning-based deliverable VMAT plan generated by prototype software for automated planning for prostate cancer patients. JOURNAL OF RADIATION RESEARCH 2023; 64:842-849. [PMID: 37607667 PMCID: PMC10516733 DOI: 10.1093/jrr/rrad058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 05/08/2023] [Indexed: 08/24/2023]
Abstract
This study aims to evaluate the dosimetric accuracy of a deep learning (DL)-based deliverable volumetric arc radiation therapy (VMAT) plan generated using DL-based automated planning assistant system (AIVOT, prototype version) for patients with prostate cancer. The VMAT data (cliDose) of 68 patients with prostate cancer treated with VMAT treatment (70-74 Gy/28-37 fr) at our hospital were used (n = 55 for training and n = 13 for testing). First, a HD-U-net-based 3D dose prediction model implemented in AIVOT was customized using the VMAT data. Thus, a predictive VMAT plan (preDose) comprising AIVOT that predicted the 3D doses was generated. Second, deliverable VMAT plans (deliDose) were created using AIVOT, the radiation treatment planning system Eclipse (version 15.6) and its vender-supplied objective functions. Finally, we compared these two estimated DL-based VMAT treatment plans-i.e. preDose and deliDose-with cliDose. The average absolute dose difference of all DVH parameters for the target tissue between cliDose and deliDose across all patients was 1.32 ± 1.35% (range: 0.04-6.21%), while that for all the organs at risks was 2.08 ± 2.79% (range: 0.00-15.4%). The deliDose was superior to the cliDose in all DVH parameters for bladder and rectum. The blinded plan scoring of deliDose and cliDose was 4.54 ± 0.50 and 5.0 ± 0.0, respectively (All plans scored ≥4 points, P = 0.03.) This study demonstrated that DL-based deliverable plan for prostate cancer achieved the clinically acceptable level. Thus, the AIVOT software exhibited a potential for automated planning with no intervention for patients with prostate cancer.
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Affiliation(s)
- Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan
| | - Yuto Kimura
- Radiation Oncology Center, Ofuna Chuo Hospital, Ofuna 6-2-24, Kamakura, Kanagawa 247-0056, Japan
| | - Ryota Tozuka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan
| | - Shohei Tanaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan
| | - Kazuhiro Arai
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan
| | - Yoshiyuki Katsuta
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan
| | - Hidetoshi Shimizu
- Department of Radiation Oncology, Aichi Cancer Center Hospital, Kanokoden 1-1, Chikusa-ku, Nagoya, Aichi, 464-8681, Japan
| | - Yuto Sugai
- Department of Radiological Technology, Keio University, Shinanomachi 35, Shinjuku-ku, Tokyo 160-8582, Japan
| | - Takaya Yamamoto
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan
| | - Rei Umezawa
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan
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Saha S, Sriram Prasath S, Arun B, Kalita SJ, Elavarasan N, Guha Adhya D, Sarkar A, Arunsingh M, Chakraborty S, Mallick I. ICON-P - A double-blind evaluation of quality improvements with individualized CONstraints from low-cost knowledge-based radiation therapy planning in prostate cancer. Tech Innov Patient Support Radiat Oncol 2023; 26:100206. [PMID: 37274093 PMCID: PMC10232660 DOI: 10.1016/j.tipsro.2023.100206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/11/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023] Open
Abstract
Purpose /Objective(S)A low-cost, prior knowledge-based individualized dose-constraint generator for organs-at-risk has been developed for prostate cancer radiation therapy (RT) planning. In this study, we aimed to evaluate the feasibility and improvements in organs-at-risk (OAR) doses in prostate cancer RT planning using this tool served on a web application. Materials And Methods A set of previously treated prostate cancer cases planned and treated with generic constraints were replanned using individualized dose constraints derived from a library of cases with similar volumes of target, OAR, and overlap regions and served on the web-based application. The goal was to assess the reduction in mean dose, specified dose volumes (V59Gy, V56Gy, V53Gy, V47Gy, and V40Gy), and generalized equivalent uniform dose (gEUD) to the rectum and bladder. Planners and assessors were blinded to the initial achieved doses and penalties. Sample size estimation was based on improvement in V53Gy for the rectum and bladder with a paired evaluation. Results Twenty-four patients were replanned. All the plans had a PTV D95 of at least 97% of the prescribed dose. The individualized OAR constraints could be met for 87.5% of patients for all dose levels. The mean dose, V59Gy, V53Gy, and V47Gy for the bladder was reduced by 7.5 Gy, 1.12%, 5.51%, and 10.53% respectively. Similarly for the rectum, the mean dose, V59Gy, V53Gy, V47Gy and was reduced by 5.5 Gy, 4.34%, 6.97%, and 11.61% respectively. All dose reductions were statistically significant. The gEUD of the bladder was reduced by 2.47 Gy (p < 0.001) and the rectum by 3.21 Gy (p < 0.001). Conclusion Treatment planning based on individualized dose constraints served on a web application is feasible and leads to improvement at clinically important dose volumes in prostate cancer RT planning. This application can be served publicly for improvements in RT plan quality.
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Affiliation(s)
- Saheli Saha
- Department of Radiation Oncology, Tata Medical Center, Kolkata, India
| | - S Sriram Prasath
- Department of Radiation Oncology, Tata Medical Center, Kolkata, India
| | - Balakrishnan Arun
- Department of Radiation Oncology, Tata Medical Center, Kolkata, India
| | | | | | - Debashree Guha Adhya
- School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, India
| | - Arnab Sarkar
- Department of Mechanical Engineering, Indian Institution of Technology (BHU) Varanasi, India
| | - Moses Arunsingh
- Department of Radiation Oncology, Tata Medical Center, Kolkata, India
| | | | - Indranil Mallick
- Department of Radiation Oncology, Tata Medical Center, Kolkata, India
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Phurailatpam R, Sah MK, Wadasadawala T, Khan A, Palottukandy J, Gayake U, Jain J, Sarin R, Pathak R, Krishnamurthy R, Joshi K, Swamidas J. Can knowledge based treatment planning of VMAT for post-mastectomy locoregional radiotherapy involving internal mammary chain and supraclavicular fossa improve performance efficiency? Front Oncol 2023; 13:991952. [PMID: 37114138 PMCID: PMC10128860 DOI: 10.3389/fonc.2023.991952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 03/06/2023] [Indexed: 04/05/2023] Open
Abstract
IntroductionTo validate and evaluate the performance of knowledge-based treatment planning for Volumetric Modulated Arc Radiotherapy for post-mastectomy loco-regional radiotherapy.Material and methodsTwo knowledge-based planning (KBP) models for different dose prescriptions were built using the Eclipse RapidPlanTM v 16.1 (Varian Medical Systems, Palo Alto, USA) utilising the plans of previously treated patients with left-sided breast cancer who had undergone irradiation of the left chest wall, internal mammary nodal (IMN) region and supra-clavicular fossa (SCF). Plans of 60 and 73 patients were used to generate the KBP models for the prescriptions of 40 Gy in 15 fractions and 26 Gy in 5 fractions, respectively. A blinded review of all the clinical plans (CLI) and KBPs was done by two experienced radiation oncology consultants. Statistical analysis of the two groups was also done using the standard two-tailed paired t-test or Wilcoxon signed rank test, and p<0.05 was considered significant.ResultsA total of 20 metrics were compared. The KBPs were found to be either better (6/20) or comparable (10/20) to the CLIs for both the regimens. Dose to heart, contralateral breast,contralateral lung were either better or comparable in the KBP plans except of ipsilateral lung. Mean dose (Gy) for the ipsilateral lung are significantly (p˂0.001) higher in KBP though the values were acceptable clinically. Plans were of similar quality as per the result of the blinded review which was conducted by slice-by-slice evaluation of dose distribution for target coverage, overdose volume and dose to the OARs. However, it was also observed that treatment times in terms of monitoring units (MUs) and complexity indices are more in CLIs as compared with KBPs (p<0.001).DiscussionKBP models for left-sided post-mastectomy loco-regional radiotherapy were developed and validated for clinical use. These models improved the efficiency of treatment delivery as well as work flow for VMAT planning involving both moderately hypo fractionated and ultra-hypo fractionated radiotherapy regimens.
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Affiliation(s)
- Reena Phurailatpam
- Department of Radiation Oncology, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
- *Correspondence: Reena Phurailatpam, ; Tabassum Wadasadawala,
| | - Muktar kumar Sah
- Department of Radiation Oncology, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Tabassum Wadasadawala
- Department of Radiation Oncology, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
- *Correspondence: Reena Phurailatpam, ; Tabassum Wadasadawala,
| | - Asfiya Khan
- Department of Radiation Oncology, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Jithin Palottukandy
- Department of Radiation Oncology, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Umesh Gayake
- Department of Radiation Oncology, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Jeevanshu Jain
- Department of Radiation Oncology, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Rajiv Sarin
- Department of Radiation Oncology, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Rima Pathak
- Department of Radiation Oncology, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Revathy Krishnamurthy
- Department of Radiation Oncology, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Kishore Joshi
- Department of Radiation Oncology, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Jamema Swamidas
- Department of Radiation Oncology, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
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12
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Fanou AM, Patatoukas G, Chalkia M, Kollaros N, Kougioumtzopoulou A, Kouloulias V, Platoni K. Implementation, Dosimetric Assessment, and Treatment Validation of Knowledge-Based Planning (KBP) Models in VMAT Head and Neck Radiation Oncology. Biomedicines 2023; 11:biomedicines11030762. [PMID: 36979740 PMCID: PMC10045933 DOI: 10.3390/biomedicines11030762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 02/21/2023] [Accepted: 02/26/2023] [Indexed: 03/06/2023] Open
Abstract
The aim of this study was to evaluate knowledge-based treatment planning (KBP) models in terms of their dosimetry and deliverability and to investigate their clinical benefits. Three H&N KBP models were built utilizing RapidPlan™, based on the dose prescription, which is given according to the planning target volume (PTV). The training set for each model consisted of 43 clinically acceptable volumetric modulated arc therapy (VMAT) plans. Model quality was assessed and compared to the delivered treatment plans using the homogeneity index (HI), conformity index (CI), structure dose difference (PTV, organ at risk—OAR), monitor units, MU factor, and complexity index. Model deliverability was assessed through a patient-specific quality assurance (PSQA) gamma index-based analysis. The dosimetric assessment showed better OAR sparing for the RapidPlan™ plans and for the low- and high-risk PTV, and the HI, and CI were comparable between the clinical and RapidPlan™ plans, while for the intermediate-risk PTV, CI was better for clinical plans. The 2D gamma passing rates for RapidPlan™ plans were similar or better than the clinical ones using the 3%/3 mm gamma-index criterion. Monitor units, the MU factors, and complexity indices were found to be comparable between RapidPlan™ and the clinical plans. Knowledge-based treatment plans can be safely adapted into clinical routines, providing improved plan quality in a time efficient way while minimizing user variability.
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Affiliation(s)
- Anna-Maria Fanou
- Medical Physics Unit, Second Department of Radiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, Haidari, 12462 Athens, Greece
- Correspondence: (A.-M.F.); (K.P.)
| | - Georgios Patatoukas
- Medical Physics Unit, Second Department of Radiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, Haidari, 12462 Athens, Greece
| | - Marina Chalkia
- Medical Physics Unit, Second Department of Radiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, Haidari, 12462 Athens, Greece
| | - Nikolaos Kollaros
- Medical Physics Unit, Second Department of Radiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, Haidari, 12462 Athens, Greece
| | - Andromachi Kougioumtzopoulou
- Radiation Therapy Unit, Second Department of Radiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, Haidari, 12462 Athens, Greece
| | - Vassilis Kouloulias
- Radiation Therapy Unit, Second Department of Radiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, Haidari, 12462 Athens, Greece
| | - Kalliopi Platoni
- Medical Physics Unit, Second Department of Radiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, Haidari, 12462 Athens, Greece
- Correspondence: (A.-M.F.); (K.P.)
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13
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Romano C, Viola P, Craus M, Macchia G, Ferro M, Bonome P, Pierro A, Buwenge M, Arcelli A, Morganti AG, Deodato F, Cilla S. Feasibility-guided automated planning for stereotactic treatments of prostate cancer. Med Dosim 2023:S0958-3947(23)00020-1. [PMID: 36990847 DOI: 10.1016/j.meddos.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 01/09/2023] [Accepted: 02/23/2023] [Indexed: 03/29/2023]
Abstract
Significant improvements in plan quality using automated planning have been previously demonstrated. The aim of this study was to develop an optimal automated class solution for stereotactic radiotherapy (SBRT) planning of prostate cancer using the new Feasibility module implemented in the pinnacle evolution. Twelve patients were retrospectively enrolled in this planning study. Five plans were designed for each patient. Four plans were automatically generated using the 4 proposed templates for SBRT optimization implemented in the new pinnacle evolution treatment planning systems, differing for different settings of dose-fallout (low, medium, high and veryhigh). Based on the obtained results, the fifth plan (feas) was generated customizing the template with the optimal criteria obtained from the previous step and integrating in the template the "a-priori" knowledge of OARs sparing based on the Feasibility module, able to estimate the best possible dose-volume histograms of OARs before starting optimization. Prescribed dose was 35 Gy to the prostate in 5 fractions. All plans were generated with a full volumetric-modulated arc therapy arc and 6MV flattening filter-free beams, and optimized to ensure the same target coverage (95% of the prescription dose to 98% of the target). Plans were assessed according to dosimetric parameters and planning and delivery efficiency. Differences among the plans were evaluated using a Kruskal-Wallis 1-way analysis of variance. The requests for more aggressive objectives for dose falloff parameters (from low to veryhigh) translated in a statistically significant improvement of dose conformity, but at the expense of a dose homogeneity. The best automated plans in terms of best trade-off between target coverage and OARs sparing among the 4 plans automatically generated by the SBRT module were the high plans. The veryhigh plans reported a significant increase of high-doses to prostate, rectum, and bladder that was considered dosimetrically and clinically unacceptable. The feas plans were optimized on the basis on high plans, reporting significant reduction of rectum irradiation; Dmean, and V18 decreased by 19% to 23% (p = 0.031) and 4% to 7% (p = 0.059), respectively. No statistically significant differences were found in femoral heads and penile bulb irradiation for all dosimetric metrics. feas plans showed a significant increase of MU/Gy (mean: 368; p = 0.004), reflecting an increased level of fluence modulation. Thanks to the new efficient optimization engines implemented in pinnacle evolution (L-BFGS and layered graph), mean planning time was decreased to less than 10 minutes for all plans and all techniques. The integration of dose-volume histograms a-priori knowledge provided by the feasibility module in the automated planning process for SBRT planning has shown to significantly improve plan quality compared to generic protocol values as inputs.
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14
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Anetai Y, Takegawa H, Koike Y, Nakamura S, Tanigawa N. Effective optimization strategy for large optimization volume object, remaining volume at risk (RVR): α-value selection and usage from generalized equivalent uniform dose (gEUD) curve deviation perspective. Phys Med Biol 2023; 68. [PMID: 36745933 DOI: 10.1088/1361-6560/acb989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 02/06/2023] [Indexed: 02/08/2023]
Abstract
Objective.A large optimization volume for intensity-modulated radiation therapy (IMRT), such as the remaining volume at risk (RVR), is traditionally unsuitable for dose-volume constraint control and requires planner-specific empirical considerations owing to the patient-specific shape. To enable less empirical optimization, the generalized equivalent uniform dose (gEUD) optimization is effective; however, the utilization of parametera-values remains elusive. Our study clarifies thea-value characteristics for optimization and to enable effectivea-value use.Approach.The gEUD can be obtained as a function of itsa-value, which is the weighted generalized mean; its curve has a continuous, differentiable, and sigmoid shape, deforming in its optimization state with retained curve characteristics. Using differential geometry, the gEUD curve changes in optimization is considered a geodesic deviation intervened by the forces between deforming and retaining the curve. The curvature and gradient of the curve are radically related to optimization. The vertex point (a=ak) was set and thea-value roles were classified into the following three parts of the curve with respect to thea-value: (i) high gradient and middle curvature, (ii) middle gradient and high curvature, and (iii) low gradient and low curvature. Then, a strategy for multiplea-values was then identified using RVR optimization.Main results.Eleven head and neck patients who underwent static seven-field IMRT were used to verify thea-value characteristics and curvature effect for optimization. The lowera-value (i) (a= 1-3) optimization was effective for the whole dose-volume range; in contrast, the effect of highera-value (iii) (a= 12-20) optimization addressed strongly the high-dose range of the dose volume. The middlea-value (ii) (arounda=ak) showed intermediate but effective high-to-low dose reduction. Thesea-value characteristics were observed as superimpositions in the optimization. Thus, multiple gEUD-based optimization was significantly superior to the exponential constraints normally applied to the RVR that surrounds the PTV, normal tissue objective (NTO), resulting in up to 25.9% and 8.1% improvement in dose-volume indices D2% and V10Gy, respectively.Significance.This study revealed an appropriatea-value for gEUD optimization, leading to favorable dose-volume optimization for the RVR region using fixed multiplea-value conditions, despite the very large and patient-specific shape of the region.
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Affiliation(s)
- Yusuke Anetai
- Department of Radiology, Kansai Medical University, Shin-machi 2-5-1, Hirakata-shi, Osaka 573-1010, Japan
| | - Hideki Takegawa
- Department of Radiology, Kansai Medical University, Shin-machi 2-5-1, Hirakata-shi, Osaka 573-1010, Japan
| | - Yuhei Koike
- Department of Radiology, Kansai Medical University, Shin-machi 2-5-1, Hirakata-shi, Osaka 573-1010, Japan
| | - Satoaki Nakamura
- Department of Radiology, Kansai Medical University, Shin-machi 2-5-1, Hirakata-shi, Osaka 573-1010, Japan
| | - Noboru Tanigawa
- Department of Radiology, Kansai Medical University, Shin-machi 2-5-1, Hirakata-shi, Osaka 573-1010, Japan
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15
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Tang C, Gong C, Liu B, Guo H, Dai Z, Yuan J, Wang X, Zhang Y. Feasibility and dosimetric evaluation of single- and multi-isocentre stereotactic body radiation therapy for multiple liver metastases. Front Oncol 2023; 13:1144784. [PMID: 37188200 PMCID: PMC10175834 DOI: 10.3389/fonc.2023.1144784] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 04/13/2023] [Indexed: 05/17/2023] Open
Abstract
Objectives Single-isocentre volumetric-modulated arc therapy (VMAT) stereotactic body radiation therapy (SBRT) improves treatment efficiency and patient compliance for patients with multiple liver metastases (MLM). However, the potential increase in dose spillage to normal liver tissue using a single-isocentre technique has not yet been studied. We comprehensively evaluated the quality of single- and multi-isocentre VMAT-SBRT for MLM and propose a RapidPlan-based automatic planning (AP) approach for MLM SBRT. Methods A total of 30 patients with MLM (two or three lesions) were selected for this retrospective study. We manually replanned all patients treated with MLM SBRT by using the single-isocentre (MUS) and multi-isocentre (MUM) techniques. Then, we randomly selected 20 MUS and MUM plans for training to generate the single-isocentre RapidPlan model (RPS) and the multi-isocentre RapidPlan model (RPM). Finally, we used data from the remaining 10 patients to validate RPS and RPM. Results Compared with MUS, MUM reduced the mean dose delivered to the right kidney by 0.3 Gy. The mean liver dose (MLD) was 2.3 Gy higher for MUS compared with MUM. However, the monitor units, delivery time, and V20Gy of normal liver (liver-gross tumour volume) for MUM were significantly higher than for MUS. Based on validation, RPS and RPM slightly improved the MLD, V20Gy, normal tissue complications, and dose sparing to the right and left kidneys and spinal cord compared with manual plans (MUS vs RPS and MUM vs RPM), but RPS and RPM significantly increased monitor units and delivery time. Conclusions The single-isocentre VMAT-SBRT approach could be used for MLM to reduce treatment time and patient comfort at the cost of a small increase in the MLD. Compared with the manual plans, RapidPlan-based plans, especially RPS, have slightly improved quality.
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Affiliation(s)
- Chunbo Tang
- Department of Oncology, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Changfei Gong
- Department of Radiation Oncology, Jiangxi Cancer Hospital, Nanchang, China
- *Correspondence: Changfei Gong, ; Yun Zhang,
| | - Biaoshui Liu
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Hailiang Guo
- Department of Oncology, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Zhongyang Dai
- Department of Oncology, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Jun Yuan
- Department of Oncology, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Xiaoping Wang
- Department of Radiation Oncology, Jiangxi Cancer Hospital, Nanchang, China
| | - Yun Zhang
- Department of Radiation Oncology, Jiangxi Cancer Hospital, Nanchang, China
- *Correspondence: Changfei Gong, ; Yun Zhang,
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Momin S, Wolf J, Roper J, Lei Y, Liu T, Bradley JD, Higgins K, Yang X, Zhang J. Enhanced cardiac substructure sparing through knowledge-based treatment planning for non-small cell lung cancer radiotherapy. Front Oncol 2022; 12:1055428. [DOI: 10.3389/fonc.2022.1055428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/10/2022] [Indexed: 12/03/2022] Open
Abstract
Radiotherapy (RT) doses to cardiac substructures from the definitive treatment of locally advanced non-small cell lung cancers (NSCLC) have been linked to post-RT cardiac toxicities. With modern treatment delivery techniques, it is possible to focus radiation doses to the planning target volume while reducing cardiac substructure doses. However, it is often challenging to design such treatment plans due to complex tradeoffs involving numerous cardiac substructures. Here, we built a cardiac-substructure-based knowledge-based planning (CS-KBP) model and retrospectively evaluated its performance against a cardiac-based KBP (C-KBP) model and manually optimized patient treatment plans. CS-KBP/C-KBP models were built with 27 previously-treated plans that preferentially spare the heart. While the C-KBP training plans were created with whole heart structures, the CS-KBP model training plans each have 15 cardiac substructures (coronary arteries, valves, great vessels, and chambers of the heart). CS-KBP training plans reflect cardiac-substructure sparing preferences. We evaluated both models on 28 additional patients. Three sets of treatment plans were compared: (1) manually optimized, (2) C-KBP model-generated, and (3) CS-KBP model-generated. Plans were normalized to receive the prescribed dose to at least 95% of the PTV. A two-tailed paired-sample t-test was performed for clinically relevant dose-volume metrics to evaluate the performance of the CS-KBP model against the C-KBP model and clinical plans, respectively. Overall results show significantly improved cardiac substructure sparing by CS-KBP in comparison to C-KBP and the clinical plans. For instance, the average left anterior descending artery volume receiving 15 Gy (V15 Gy) was significantly lower (p < 0.01) for CS-KBP (0.69 ± 1.57 cc) compared to the clinical plans (1.23 ± 1.76 cc) and C-KBP plans (1.05 ± 1.68 cc). In conclusion, the CS-KBP model significantly improved cardiac-substructure sparing without exceeding the tolerances of other OARs or compromising PTV coverage.
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A comparison of in-house and shared RapidPlan models for prostate radiation therapy planning. Phys Eng Sci Med 2022; 45:1029-1041. [PMID: 36063348 DOI: 10.1007/s13246-022-01151-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 06/03/2022] [Indexed: 12/15/2022]
Abstract
Knowledge-based planning (KBP) can increase plan quality, consistency and efficiency. In this study, we assess the success of a using a publicly available KBP model compared with developing an in-house model for prostate cancer radiotherapy using a single, commercially available treatment planning system based on the ability of the model to achieve the centre's planning goals. Two radiation oncology centres each created a prostate cancer KBP model using the Eclipse RapidPlan software. These two models and a third publicly-available, shared model were tested at three centres in a retrospective planning study. The publicly-available model achieved lower rectum doses than the other two models. However, the planning-target-volume (PTV) doses did not meet the local planning goals and the model could not be adjusted to correct this. As a result, the plans most likely to satisfy local planning goals and requirements were created using an in-house model. For centres without an existing in-house model, a model created by another centre with similar planning goals was found to be preferred. Variations in local planning practices including contouring, treatment technique and planning goals can influence the relative performance of KBP. The value of publicly available KBP models could be enhanced through standardisation of planning goals and contouring guidelines, providing information related to the planning goals used to create the model and increased flexibility to allow local adaptation of the KBP model.
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Luca K, Roper J, Wolf J, Kayode O, Bradley J, Stokes WA, Zhang J. Evaluating the plan quality of a general head-and-neck knowledge-based planning model versus separate unilateral/bilateral models. Med Dosim 2022; 48:44-50. [PMID: 36400649 DOI: 10.1016/j.meddos.2022.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 09/22/2022] [Accepted: 10/16/2022] [Indexed: 11/18/2022]
Abstract
The implementation of knowledge-based planning (KBP) continues to grow in radiotherapy clinics. KBP guides radiation treatment design by generating clinically acceptable plans in a timely and resource-efficient manner. The role of multiple KBP models tailored for variations within a disease site remains undefined in part because of the substantial effort and number of training cases required to create a high-quality KBP model. In this study, our aim was to explore whether site-specific KBP models lead to clinically meaningful differences in plan quality for head-and-neck (HN) patients when compared to a general model. One KBP model was created from prior volumetric-modulated arc therapy (VMAT) cases that treated unilateral HN lymph nodes while another model was created from VMAT cases that treated bilateral HN nodes. Thirty cases from each model (60 cases total) were randomly selected to create a third, general model. These models were applied to 60 HN test cases - 30 unilateral and 30 bilateral - to generate 180 VMAT plans in Eclipse. Clinically relevant dose metrics were compared between models. Paired-sample t-tests were used for statistical analysis, with the threshold for statistical significance set a priori at 0.007, taking into consideration multiple hypothesis testing to avoid type I error. For unilateral test cases, the unilateral model-generated plans had significantly lower spinal cord maximum doses (12.1 Gy vs 19.3 Gy, p < 0.001) and oral cavity mean doses (20.8 Gy vs 23.0 Gy, p < 0.001), compared with the bilateral model-generated plans. The unilateral and general models generated comparable plans for unilateral HN test cases. For bilateral test cases, the bilateral model created plans had significantly lower brainstem maximum doses (10.8 Gy vs 12.2 Gy, p < 0.001) and parotid mean doses (24.0 Gy vs 25.5 Gy, p < 0.001) when compared to the unilateral model. Right parotid mean doses were lower for bilateral model plans compared to general model plans (23.8 Gy vs 24.4 Gy). The general model created plans with significantly lower brainstem maximum doses (10.3 Gy vs 10.8 Gy) and oral cavity mean doses (35.3 Gy vs 36.7 Gy) when compared with bilateral model-generated plans. The general model outperformed the bilateral model in several dose metrics but they were not deemed clinically significant. For both case sets, the unilateral and general model created plans had higher monitor units when compared to the bilateral model, likely due to more stringent constraint settings. All other dose metrics were comparable. This study demonstrates that a balanced general HN model created using carefully curated treatment plans can produce high quality plans comparable to dedicated unilateral and bilateral models.
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Affiliation(s)
- Kirk Luca
- Emory Department of Radiation Oncology, Atlanta, GA, USA.
| | - Justin Roper
- Emory Department of Radiation Oncology, Atlanta, GA, USA
| | - Jonathan Wolf
- Emory Department of Radiation Oncology, Atlanta, GA, USA
| | | | | | | | - Jiahan Zhang
- Emory Department of Radiation Oncology, Atlanta, GA, USA
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Multi-institution model (big model) versus single-institution model of knowledge-based volumetric modulated arc therapy (VMAT) planning for prostate cancer. Sci Rep 2022; 12:15282. [PMID: 36088382 PMCID: PMC9464226 DOI: 10.1038/s41598-022-19498-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 08/30/2022] [Indexed: 11/08/2022] Open
Abstract
AbstractWe established a multi-institution model (big model) of knowledge-based treatment planning with over 500 treatment plans from five institutions in volumetric modulated arc therapy (VMAT) for prostate cancer. This study aimed to clarify the efficacy of using a large number of registered treatment plans for sharing the big model. The big model was created with 561 clinically approved VMAT plans for prostate cancer from five institutions (A: 150, B: 153, C: 49, D: 60, and E: 149) with different planning strategies. The dosimetric parameters of planning target volume (PTV), rectum, and bladder for two validation VMAT plans generated with the big model were compared with those from each institutional model (single-institution model). The goodness-of-fit of regression lines (R2 and χ2 values) and ratios of the outliers of Cook’s distance (CD) > 4.0, modified Z-score (mZ) > 3.5, studentized residual (SR) > 3.0, and areal difference of estimate (dA) > 3.0 for regression scatter plots in the big model and single-institution model were also evaluated. The mean ± standard deviation (SD) of dosimetric parameters were as follows (big model vs. single-institution model): 79.0 ± 1.6 vs. 78.7 ± 0.5 (D50) and 0.13 ± 0.06 vs. 0.13 ± 0.07 (Homogeneity Index) for the PTV; 6.6 ± 4.0 vs. 8.4 ± 3.6 (V90) and 32.4 ± 3.8 vs. 46.6 ± 15.4 (V50) for the rectum; and 13.8 ± 1.8 vs. 13.3 ± 4.3 (V90) and 39.9 ± 2.0 vs. 38.4 ± 5.2 (V50) for the bladder. The R2 values in the big model were 0.251 and 0.755 for rectum and bladder, respectively, which were comparable to those from each institution model. The respective χ2 values in the big model were 1.009 and 1.002, which were closer to 1.0 than those from each institution model. The ratios of the outliers in the big model were also comparable to those from each institution model. The big model could generate a comparable VMAT plan quality compared with each single-institution model and therefore could possibly be shared with other institutions.
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Ayuthaya IIN, Suriyapee S, Sanghangthum T. Validation of RapidPlan Knowledge-Based Model for Volumetric-Modulated Arc Therapy in Prostate Cancer. J Med Phys 2022; 47:250-255. [PMID: 36684695 PMCID: PMC9847006 DOI: 10.4103/jmp.jmp_138_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 11/10/2022] Open
Abstract
Aims and Objectives This study aims to investigate the viability of using RapidPlan (RP) knowledge-based (KB) treatment plans to initiate the new prostate volumetric-modulated arc therapy (VMAT) plans. Materials and Methods The planning data for 120 prostate VMAT treatment plans were entered into the RP system's database. The database of previous VMAT plans was divided into four model groups for training in the RP system. The models were based on the numbers of 20, 60, and 120 prostate VMAT plans. The model of 120 plans used automated priority and manual priority for the optimization process. The models of 20 and 60 plans used only manual priority for optimization. Each model was validated on 15 cases of new prostate cancer patients by comparing RP model plans against manual clinical plans optimized according to the clinical dose constraints. Results The RP models can estimate the dose comparable target volume to the manual clinical plan, which evaluated values of Dmax, D95%, D98%, HI, and CI and showed comparable results. For the normal organ doses of the bladder, rectum, penile bulb, and femoral head, all RP models exhibited a comparable or better dose than the manual clinical plan, except for the RP models using the automated priority for the optimization process, which cannot control the rectum dose below the dose constraints. Conclusions The Varian RP KB planning can produce comparable doses or better doses with the clinical manual in a single optimization, although the RP model uses a minimum requirement of the planning number for the model training. The RP models can enhance the efficacy and quality of plans, which depend on the number of VMAT plans used in RP model training for prostate cancer.
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Affiliation(s)
- Isra Israngkul Na Ayuthaya
- Department of Radiology, Division of Therapeutic Radiology and Oncology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Sivalee Suriyapee
- Division of Therapeutic Radiology and Oncology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Taweap Sanghangthum
- Division of Therapeutic Radiology and Oncology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
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21
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Sprouts D, Gao Y, Wang C, Jia X, Shen C, Chi Y. The development of a deep reinforcement learning network for dose-volume-constrained treatment planning in prostate cancer intensity modulated radiotherapy. Biomed Phys Eng Express 2022; 8. [PMID: 35523130 DOI: 10.1088/2057-1976/ac6d82] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 05/06/2022] [Indexed: 11/11/2022]
Abstract
Although commercial treatment planning systems (TPSs) can automatically solve the optimization problem for treatment planning, human planners need to define and adjust the planning objectives/constraints to obtain clinically acceptable plans. Such a process is labor-intensive and time-consuming. In this work, we show an end-to-end study to train a deep reinforcement learning (DRL) based virtual treatment planner (VTP) that can behave like a human to operate a dose-volume constrained treatment plan optimization engine following the parameters used in Eclipse TPS for high-quality treatment planning. We considered the prostate cancer IMRT treatment plan as the testbed. The VTP took the dose-volume histogram (DVH) of a plan as input and predicted the optimal strategy for constraint adjustment to improve the plan quality. The training of VTP followed the state-of-the-art Q-learning framework. Experience replay was implemented with epsilon-greedy search to explore the impacts of taking different actions on a large number of automatically generated plans, from which an optimal policy can be learned. Since a major computational cost in training was to solve the plan optimization problem repeatedly, we implemented a graphical processing unit (GPU)-based technique to improve the efficiency by 2-fold. Upon the completion of training, the established VTP was deployed to plan for an independent set of 50 testing patient cases. Connecting the established VTP with the Eclipse workstation via the application programming interface, we tested the performance the VTP in operating Eclipse TPS for automatic treatment planning with another two independent patient cases. Like a human planner, VTP kept adjusting the planning objectives/constraints to improve plan quality until the plan was acceptable or the maximum number of adjustment steps was reached under both scenarios. The generated plans were evaluated using the ProKnow scoring system. The mean plan score (± standard deviation) of the 50 testing cases were improved from 6.18 ± 1.75 to 8.14 ± 1.27 by the VTP, with 9 being the maximal score. As for the two cases under Eclipse dose optimization, the plan scores were improved from 8 to 8.4 and 8.7 respectively by the VTP. These results indicated that the proposed DRL-based VTP was able to operate the in-house dose-volume constrained TPS and Eclipse TPS to automatically generate high-quality treatment plans for prostate cancer IMRT.
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Affiliation(s)
- Damon Sprouts
- Department of Physics, The University of Texas at Arlington, Arlington, TX 76019, United States of America
| | - Yin Gao
- Innovative Technology of Radiotherapy Computation and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75287, United States of America
| | - Chao Wang
- Innovative Technology of Radiotherapy Computation and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75287, United States of America
| | - Xun Jia
- Innovative Technology of Radiotherapy Computation and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75287, United States of America
| | - Chenyang Shen
- Innovative Technology of Radiotherapy Computation and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75287, United States of America
| | - Yujie Chi
- Department of Physics, The University of Texas at Arlington, Arlington, TX 76019, United States of America
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22
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Clinical evaluation of autonomous, unsupervised planning integrated in MR-guided radiotherapy for prostate cancer. Radiother Oncol 2022; 168:229-233. [DOI: 10.1016/j.radonc.2022.01.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 01/04/2022] [Accepted: 01/25/2022] [Indexed: 01/18/2023]
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Ono T, Nakamura M, Ono Y, Nakamura K, Mizowaki T. Development of a plan complexity mitigation algorithm based on gamma passing rate predictions for volumetric-modulated arc therapy. Med Phys 2022; 49:1793-1802. [PMID: 35064567 DOI: 10.1002/mp.15466] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 12/28/2021] [Accepted: 12/29/2021] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND AND PURPOSE Volumetric-modulated arc therapy (VMAT) is a complex rotational therapy technique in which highly conformal dose distribution can be realized by varying the speed of gantry rotation, multileaf collimator (MLC) shape, and dose rate. However, the complexity of the technique creates a discrepancy between the calculated and measured doses. Thus, to mitigate the plan complexity in VMAT, this study aimed to develop an algorithm and evaluate its usefulness by conducting a feasibility study. MATERIALS AND METHODS A total of 50 patients who underwent VMAT between September 2015 and December 2020 were arbitrarily selected for this study. Specifically, patients with less than 85% gamma passing rate (GPR) at 5%/1 mm or 3%/2 mm criterion were selected randomly. Using the GPR prediction model, problematic MLC positions that contribute to a decrease in GPR were identified. Those problematic MLC positions were optimized using a limited nonlinear algorithm under mechanical limitations. Additionally, the dose prescription for the target was re-normalized. The VMAT modulated complexity score (MCSv ), averaged aperture area (AA), and monitor unit per gray (MU/Gy) were evaluated as plan complexity parameters. Calculated doses in patient geometry were evaluated for the target and its surrounding region. In addition, an ArcCHECK cylindrical diode array was used to measure the dose, and GPRs at 5%/1 mm and 3%/2 mm criteria were evaluated to analyze the difference between the mitigated and original plans. The difference was calculated using the mean ± standard deviation. RESULTS The differences between the MCSv , AA, and MU/cGy values for the mitigated and original plans were 0.8 ± 1.7 (× 10-2 ), 42.7 ± 57.9, and -5.6 ± 8.5, respectively. Regarding the calculated dose, the dose volume parameters were consistent within 1% for the target and the surrounding region. The differences between the mitigated and original plans were 1.8 ± 2.9% and 1.3 ± 1.8% for GPRs at 5%/1 mm and 3%/2 mm, respectively. CONCLUSIONS This feasibility study resulted in the development of an algorithm with the potential to mitigate plan complexity and improve the GPR for VMAT under minor leaf position modifications. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Tomohiro Ono
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mitsuhiro Nakamura
- Department of Information Technology and Medical Engineering, Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuka Ono
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kiyonao Nakamura
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takashi Mizowaki
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Kusters M, Miki K, Bouwmans L, Bzdusek K, van Kollenburg P, Smeenk RJ, Monshouwer R, Nagata Y. Evaluation of two independent dose prediction methods to personalize the automated radiotherapy planning process for prostate cancer. Phys Imaging Radiat Oncol 2022; 21:24-29. [PMID: 35146138 PMCID: PMC8819373 DOI: 10.1016/j.phro.2022.01.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 01/26/2022] [Accepted: 01/26/2022] [Indexed: 11/18/2022] Open
Abstract
Background and purpose Currently, automatic approaches for radiotherapy planning are widely used, however creation of high quality treatment plans is still challenging. In this study, two independent dose prediction methods were used to personalize the initial settings for the automated planning template for optimizing prostate cancer treatment plans. This study evaluated the dose metrics of these plans comparing both methods with the current clinical automated prostate cancer treatment plans. Material and methods Datasets of 20 high-risk prostate cancer treatment plans were taken from our clinical database. The prescription dose for these plans was 70 Gy given in fractions of 2.5 Gy. Plans were replanned using the current clinical automated treatment and compared with two personalized automated planning methods. The feasibility dose volume histogram (FDVH) and modified filter back projection (mFBP) methods were used to calculate independent dose predictions. Parameters for the initial objective values of the planning template were extracted from these predictions and used to personalize the optimization of the automated planning process. Results The current automated replanned clinical plans and the automated plans optimized with the personalized template methods fulfilled the clinical dose criteria. For both methods a reduction in the average mean dose of the rectal wall was found, from 22.5 to 20.1 Gy for the FDVH and from 22.5 to 19.6 Gy for the mFBP method. Conclusions With both dose-prediction methods the initial settings of the template could be personalized. Hereby, the average dose to the rectal wall was reduced compared to the standard template method.
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Pallotta S, Marrazzo L, Calusi S, Castriconi R, Fiorino C, Loi G, Fiandra C. Implementation of automatic plan optimization in Italy: Status and perspectives. Phys Med 2021; 92:86-94. [PMID: 34875426 DOI: 10.1016/j.ejmp.2021.11.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/20/2021] [Accepted: 11/24/2021] [Indexed: 01/04/2023] Open
Abstract
PURPOSE To investigate and report on the diffusion and clinical use of automated radiotherapy planning systems in Italy and to assess the perspectives of the community of Italian medical physicists involved in radiotherapy on the use of these tools. MATERIALS AND METHODS A survey of medical physicists (one per Institute) of 175 radiotherapy centers in Italy was conducted between February 21st and April 1st, 2021. The information collected included the institute's characteristics, plan activity, availability/use of automatic tools and related issues regarding satisfaction, criticisms, expectations, and perceived professional modifications. Responses were analysed, including the impact of a few variables such as the institute type and experience. RESULTS 125 of the centers (71%) answered the survey, with regional variability (range: 47%-100%); among these, 49% have a TPS with some automatic option. Clinical use of automatic planning is present in 33% of the centers, with 13% applying it in >50% of their plans. Among the 125 responding centres the most used systems are Pinnacle (16%), Raystation (9%) and Eclipse (4%). The majority of participants consider the use of automated techniques to be beneficial, while only 1% do not see any advantage; 83% of respondents see the possibility of enriching their professional role as a potential benefit, while 3% see potential threats. CONCLUSIONS Our survey shows that 49% of the responding centres have an automatic planning solution although clinically used in only 33% of the cases. Most physicists consider the use of automated techniques to be beneficial and show a prevalently positive attitude.
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Affiliation(s)
- Stefania Pallotta
- University of Florence, Department of Biomedical, Experimental and Clinical Sciences "Mario Serio", Florence, Italy; Medical Physics Unit, AOU Careggi, Florence, Italy.
| | | | - Silvia Calusi
- University of Florence, Department of Biomedical, Experimental and Clinical Sciences "Mario Serio", Florence, Italy
| | | | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - Gianfranco Loi
- Medical Physics, AOU Maggiore della Carità, Novara, Italy
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Xu Y, Brovold N, Cyriac J, Bossart E, Padgett K, Butkus M, Diwanj T, King A, Dal Pra A, Abramowitz M, Pollack A, Dogan N. Assessment of Knowledge-Based Planning for Prostate Intensity Modulated Proton Therapy. Int J Part Ther 2021; 8:62-72. [PMID: 34722812 PMCID: PMC8489488 DOI: 10.14338/ijpt-20-00088.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 04/07/2021] [Indexed: 02/03/2023] Open
Abstract
Purpose To assess the performance of a proton-specific knowledge based planning (KBPP) model in creation of robustly optimized intensity-modulated proton therapy (IMPT) plans for treatment of patients with prostate cancer. Materials and Methods Forty-five patients with localized prostate cancer, who had previously been treated with volumetric modulated arc therapy, were selected and replanned with robustly optimized IMPT. A KBPP model was generated from the results of 30 of the patients, and the remaining 15 patient results were used for validation. The KBPP model quality and accuracy were evaluated with the model-provided organ-at-risk regression plots and metrics. The KBPP quality was also assessed through comparison of expert and KBPP-generated IMPT plans for target coverage and organ-at-risk sparing. Results The resulting R 2 (mean ± SD, 0.87 ± 0.07) between dosimetric and geometric features, as well as the χ2 test (1.17 ± 0.07) between the original and estimated data, showed the model had good quality. All the KBPP plans were clinically acceptable. Compared with the expert plans, the KBPP plans had marginally higher dose-volume indices for the rectum V65Gy (0.8% ± 2.94%), but delivered a lower dose to the bladder (-1.06% ± 2.9% for bladder V65Gy). In addition, KBPP plans achieved lower hotspot (-0.67Gy ± 2.17Gy) and lower integral dose (-0.09Gy ± 0.3Gy) than the expert plans did. Moreover, the KBPP generated better plans that demonstrated slightly greater clinical target volume V95 (0.1% ± 0.68%) and lower homogeneity index (-1.13 ± 2.34). Conclusions The results demonstrated that robustly optimized IMPT plans created by the KBPP model are of high quality and are comparable to expert plans. Furthermore, the KBPP model can generate more-robust and more-homogenous plans compared with those of expert plans. More studies need to be done for the validation of the proton KBPP model at more-complicated treatment sites.
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Affiliation(s)
- Yihang Xu
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Nellie Brovold
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Jonathan Cyriac
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Elizabeth Bossart
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Kyle Padgett
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Michael Butkus
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Tejan Diwanj
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Adam King
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alan Dal Pra
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Matt Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Nesrin Dogan
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
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Apaza Blanco OA, Almada MJ, Garcia Andino AA, Zunino S, Venencia D. Knowledge-Based Volumetric Modulated Arc Therapy Treatment Planning for Breast Cancer. J Med Phys 2021; 46:334-340. [PMID: 35261504 PMCID: PMC8853452 DOI: 10.4103/jmp.jmp_51_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 07/19/2021] [Accepted: 07/21/2021] [Indexed: 11/24/2022] Open
Abstract
Purpose: To create and to validate knowledge-based volumetric modulated arc therapy (VMAT) models for breast cancer treatments without lymph node irradiation. Materials and Methods: One hundred VMAT-based breast plans (manual plans [MP]) were selected to create two knowledge-based VMAT models (breast left and breast right) using RapidPlan™. The plans were generated on Eclipse v15.5 (Varian Medical Systems, Palo Alto, CA) with 6 MV of a Novalis Tx equipped with a high-resolution multileaf collimator. The models were verified based on goodness-of-fit statistics using the coefficients of determination (R2) and Chi-square (χ2), and the goodness-of-estimation statistics through the mean square error (MSE). Geometrical and dosimetrical constraints were identified and removed from the RP models using statistical evaluation metrics and plots. For validation, 20 plans that integrate the models and 20 plans that do not were reoptimized with RP (closed and opened validation). Dosimetrical parameters of interest were used to compare MP versus RP plans for the Heart, Homolateral_Lung, Contralateral_Lung, and Contralateral_Breast. Optimization planning time and user independency were also analyzed. Results: The most unfavorable results of R2 in both models for the organs at risk were as follows: for Contralateral_Lung 0.51 in RP right breast (RP_RB) and for Heart 0.60 in RP left breast (RP_LB). The most unfavorable results of χ2 test were: for Contralateral_Breast 1.02 in RP_RB and for Heart 1.03 in RP_LB. These goodness-of-fit results show that no overfitting occurred in either of the models. There were no unfavorable results of mean square error (MSE, all < 0.05) in any of the two models. These goodness-of-estimation results show that the models have good estimation power. For closed validation, significant differences were found in RP_RB for Homolateral_Lung (all P ≤ 0.001), and in the RP_LB differences were found for the heart (all P ≤ 0.04) and for Homolateral_Lung (all P ≤ 0.022). For open validation, no statistically significant differences were obtained in either of the models. RP models had little impact on reducing optimization planning times for expert planners; nevertheless, the result showed a 30% reduction time for beginner planners. The use of RP models generates high-quality plans, without differences from the planner experience. Conclusion: Two RP models for breast cancer treatment using VMAT were successfully implemented. The use of RP models for breast cancer reduces the optimization planning time and improves the efficiency of the treatment planning process while ensuring high-quality plans.
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Affiliation(s)
- Oscar Abel Apaza Blanco
- Department of Medical Physics, Instituto Zunino - Fundación Marie Curie, Obispo Oro 423, X5000 BFI, Córdoba, Argentina
| | - María José Almada
- Department of Medical Physics, Instituto Zunino - Fundación Marie Curie, Obispo Oro 423, X5000 BFI, Córdoba, Argentina
| | - Albin Ariel Garcia Andino
- Department of Medical Physics, Instituto Zunino - Fundación Marie Curie, Obispo Oro 423, X5000 BFI, Córdoba, Argentina
| | - Silvia Zunino
- Department of Medical Physics, Instituto Zunino - Fundación Marie Curie, Obispo Oro 423, X5000 BFI, Córdoba, Argentina
| | - Daniel Venencia
- Department of Medical Physics, Instituto Zunino - Fundación Marie Curie, Obispo Oro 423, X5000 BFI, Córdoba, Argentina
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Nitta Y, Ueda Y, Isono M, Ohira S, Masaoka A, Karino T, Inui S, Miyazaki M, Teshima T. Customization of a Model For Knowledge-Based Planning to Achieve Ideal Dose Distributions in Volume Modulated arc Therapy for Pancreatic Cancers. J Med Phys 2021; 46:66-72. [PMID: 34566285 PMCID: PMC8415244 DOI: 10.4103/jmp.jmp_76_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 04/27/2021] [Accepted: 04/27/2021] [Indexed: 11/20/2022] Open
Abstract
Purpose: To evaluate customizing a knowledge-based planning (KBP) model using dosimetric analysis for volumetric modulated arc therapy for pancreatic cancer. Materials and Methods: The first model (M1) using 56 plans and the second model (M2) using 31 plans were created in the first 7 months of the study. The ratios of volume of both kidneys overlapping the expanded planning target volume to the total volume of both kidneys (Voverlap/Vwhole) were calculated in all cases to customize M1. Regression lines were derived from Voverlap/Vwhole and mean dose to both kidneys. The third model (M3) was created using 30 plans which data put them below the regression line. For validation, KBP was performed with the three models on 21 patients. Results: V18 of the left kidney for M1 plans was 7.3% greater than for clinical plans. Dmean of the left kidney for M2 plans was 2.2% greater than for clinical plans. There was no significant difference between all kidney doses in M3 and clinical plans. Dmean of the left kidney for M2 plans was 2.2% greater than for clinical plans. Dmean to both kidneys did not differ significantly between the three models in validation plans with Voverlap/Vwhole lower than average. In plans with larger than average volumes, the Dmean of validation plans created by M3 was significantly lower for both kidneys by 1.7 and 0.9 Gy than with M1 and M2, respectively. Conclusions: Selecting plans to register in a model by analyzing dosimetry and geometry is an effective means of improving the KBP model.
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Affiliation(s)
- Yuya Nitta
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Yoshihiro Ueda
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Masaru Isono
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Shingo Ohira
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Akira Masaoka
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Tsukasa Karino
- Department of Radiology, Osaka Women's and Children's Hospital, Osaka, Japan
| | - Shoki Inui
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan.,Department of Medical Physics and Engineering, Osaka University Graduate School, Osaka, Japan
| | - Masayoshi Miyazaki
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan.,Department of Radiology, Hyogo College of Medicine, Hyogo, Japan
| | - Teruki Teshima
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
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Dosimetric comparison of RapidPlan and manually optimised volumetric modulated arc therapy plans in prostate cancer. JOURNAL OF RADIOTHERAPY IN PRACTICE 2021. [DOI: 10.1017/s1460396920000345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
AbstractPurpose:The aim of this study was to evaluate whether RapidPlan (RP) could generate clinically acceptable prostate volumetric modulated arc therapy (VMAT) plans.Methods:The in-house RP model was used to generate VMAT plans for 50 previously treated prostate cancer patients, with no additional optimisation being performed. The VMAT plans that were generated using the RP model were compared with the patients’ previous, manually optimised clinical plans (MP), none of which had been used for the development of the in-house RP prostate model. Differences between RP and MP in planning target volume (PTV) doses, organs at risk (OAR) sparing, monitor units (MU) and planning time required to produce treatment plans were analysed. Assessment of PTV doses was based on the conformation number (CN), homogeneity index (HI), D2%, D99% and the mean dose of the PTV. The OAR doses evaluated were the rectal V50 Gy, V65 Gy, V70 Gy and the mean dose, the bladder V65 Gy, V70 Gy and the mean dose, and the mean dose to both femurs.Results:D99% and mean dose of the PTV were lower for RP than for MP (p = 0·006 and p = 0·040, respectively).V50 Gy, V65 Gy and the mean dose to rectum were lower in RP than in MP (p < 0·001). V65 Gy, V70 Gy and the mean dose to bladder were lower in RP than in MP (p < 0·001). RP had enhanced the sparing of both femurs (p < 0·001) and significantly reduced the planning time to less than 5% of the time taken with MP. MU in RP was significantly higher than MP by an average of 52·5 MU (p < 0·001) and 46 out of the 50 RP plans were approved by the radiation oncologist.Conclusion:This study has demonstrated that VMAT plans generated using an in-house RP prostate model in a single optimisation for prostate patients were clinically acceptable with comparable or better plan quality compared to MP. RP can add value and improve treatment planning efficiency in a high-throughput radiotherapy department through reduced plan optimisation time while maintaining consistency in the plan quality.
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Deep learning method for prediction of patient-specific dose distribution in breast cancer. Radiat Oncol 2021; 16:154. [PMID: 34404441 PMCID: PMC8369791 DOI: 10.1186/s13014-021-01864-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 07/19/2021] [Indexed: 11/10/2022] Open
Abstract
Background Patient-specific dose prediction improves the efficiency and quality of radiation treatment planning and reduces the time required to find the optimal plan. In this study, a patient-specific dose prediction model was developed for a left-sided breast clinical case using deep learning, and its performance was compared with that of conventional knowledge-based planning using RapidPlan™. Methods Patient-specific dose prediction was performed using a contour image of the planning target volume (PTV) and organs at risk (OARs) with a U-net-based modified dose prediction neural network. A database of 50 volumetric modulated arc therapy (VMAT) plans for left-sided breast cancer patients was utilized to produce training and validation datasets. The dose prediction deep neural network (DpNet) feature weights of the previously learned convolution layers were applied to the test on a cohort of 10 test sets. With the same patient data set, dose prediction was performed for the 10 test sets after training in RapidPlan. The 3D dose distribution, absolute dose difference error, dose-volume histogram, 2D gamma index, and iso-dose dice similarity coefficient were used for quantitative evaluation of the dose prediction. Results The mean absolute error (MAE) and one standard deviation (SD) between the clinical and deep learning dose prediction models were 0.02 ± 0.04%, 0.01 ± 0.83%, 0.16 ± 0.82%, 0.52 ± 0.97, − 0.88 ± 1.83%, − 1.16 ± 2.58%, and − 0.97 ± 1.73% for D95%, Dmean in the PTV, and the OARs of the body, left breast, heart, left lung, and right lung, respectively, and those measured between the clinical and RapidPlan dose prediction models were 0.02 ± 0.14%, 0.87 ± 0.63%, − 0.29 ± 0.98%, 1.30 ± 0.86%, − 0.32 ± 1.10%, 0.12 ± 2.13%, and − 1.74 ± 1.79, respectively. Conclusions In this study, a deep learning method for dose prediction was developed and was demonstrated to accurately predict patient-specific doses for left-sided breast cancer. Using the deep learning framework, the efficiency and accuracy of the dose prediction were compared to those of RapidPlan. The doses predicted by deep learning were superior to the results of the RapidPlan-generated VMAT plan.
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Nakamura K, Okuhata K, Tamura M, Otsuka M, Kubo K, Ueda Y, Nakamura Y, Nakamatsu K, Tanooka M, Monzen H, Nishimura Y. An updating approach for knowledge-based planning models to improve plan quality and variability in volumetric-modulated arc therapy for prostate cancer. J Appl Clin Med Phys 2021; 22:113-122. [PMID: 34338435 PMCID: PMC8425874 DOI: 10.1002/acm2.13353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/22/2021] [Accepted: 06/23/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE The purpose of this study was to compare the dose-volume parameters and regression scatter plots of the iteratively improved RapidPlan (RP) models, specific knowledge-based planning (KBP) models, in volumetric-modulated arc therapy (VMAT) for prostate cancer over three periods. METHODS A RP1 model was created from 47 clinical intensity-modulated radiation therapy (IMRT)/VMAT plans. A RP2 model was created to exceed dosimetric goals which set as the mean values +1SD of the dose-volume parameters of RP1 (50 consecutive new clinical VMAT plans). A RP3 model was created with more strict dose constraints for organs at risks (OARs) than RP1 and RP2 models (50 consecutive anew clinical VMAT plans). Each RP model was validated against 30 validation plans (RP1, RP2, and RP3) that were not used for model configuration, and the dose-volume parameters were compared. The Cook's distances of regression scatterplots of each model were also evaluated. RESULTS Significant differences (p < 0.05) between RP1 and RP2 were found in Dmean (101.5% vs. 101.9%), homogeneity index (3.90 vs. 4.44), 95% isodose conformity index (1.22 vs. 1.20) for the target, V40Gy (47.3% vs. 45.7%), V60Gy (27.9% vs. 27.1%), V70Gy (16.4% vs. 15.2%), and V78Gy (0.4% vs. 0.2%) for the rectal wall, and V40Gy (43.8% vs. 41.8%) and V70Gy (21.3% vs. 20.5%) for the bladder wall, whereas only V70Gy (15.2% vs. 15.8%) of the rectal wall differed significantly between RP2 and RP3. The proportions of cases with a Cook's distance of <1.0 (RP1, RP2, and RP3 models) were 55%, 78%, and 84% for the rectal wall, and 77%, 68%, and 76% for the bladder wall, respectively. CONCLUSIONS The iteratively improved RP models, reflecting the clear dosimetric goals based on the RP feedback (dose-volume parameters) and more strict dose constraints for the OARs, generated superior dose-volume parameters and the regression scatterplots in the model converged. This approach could be used to standardize the inverse planning strategies.
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Affiliation(s)
- Kenji Nakamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osakasayama, Japan.,Department of Radiotherapy, Takarazuka City Hospital, Kohama, Takarazuka, Japan
| | - Katsuya Okuhata
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osakasayama, Japan
| | - Mikoto Tamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osakasayama, Japan
| | - Masakazu Otsuka
- Department of Radiology, Kindai University Hospital, Osakasayama, Japan
| | - Kazuki Kubo
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osakasayama, Japan
| | - Yoshihiro Ueda
- Department of Radiation Oncology, Osaka International Cancer Institute, Chuo-ku, Japan
| | - Yasunori Nakamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osakasayama, Japan
| | - Kiyoshi Nakamatsu
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osakasayama, Japan
| | - Masao Tanooka
- Department of Radiotherapy, Takarazuka City Hospital, Kohama, Takarazuka, Japan
| | - Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osakasayama, Japan
| | - Yasumasa Nishimura
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osakasayama, Japan
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Momin S, Fu Y, Lei Y, Roper J, Bradley JD, Curran WJ, Liu T, Yang X. Knowledge-based radiation treatment planning: A data-driven method survey. J Appl Clin Med Phys 2021; 22:16-44. [PMID: 34231970 PMCID: PMC8364264 DOI: 10.1002/acm2.13337] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 04/26/2021] [Accepted: 06/02/2021] [Indexed: 12/18/2022] Open
Abstract
This paper surveys the data-driven dose prediction methods investigated for knowledge-based planning (KBP) in the last decade. These methods were classified into two major categories-traditional KBP methods and deep-learning (DL) methods-according to their techniques of utilizing previous knowledge. Traditional KBP methods include studies that require geometric or anatomical features to either find the best-matched case(s) from a repository of prior treatment plans or to build dose prediction models. DL methods include studies that train neural networks to make dose predictions. A comprehensive review of each category is presented, highlighting key features, methods, and their advancements over the years. We separated the cited works according to the framework and cancer site in each category. Finally, we briefly discuss the performance of both traditional KBP methods and DL methods, then discuss future trends of both data-driven KBP methods to dose prediction.
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Affiliation(s)
- Shadab Momin
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Yabo Fu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Jeffrey D. Bradley
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Walter J. Curran
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
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Zhong Y, Yu L, Zhao J, Fang Y, Yang Y, Wu Z, Wang J, Hu W. Clinical Implementation of Automated Treatment Planning for Rectum Intensity-Modulated Radiotherapy Using Voxel-Based Dose Prediction and Post-Optimization Strategies. Front Oncol 2021; 11:697995. [PMID: 34249757 PMCID: PMC8264432 DOI: 10.3389/fonc.2021.697995] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 06/11/2021] [Indexed: 01/11/2023] Open
Abstract
Purpose This study aims to demonstrate the feasibility of clinical implementation of automated treatment planning (ATP) using voxel-based dose prediction and post-optimization strategies for rectal cancer on uRT (United Imaging Healthcare, Shanghai, China) treatment planning system. Methods A total of 180 previously treated rectal cancer cases were enrolled in this study, including 160 cases for training, 10 for validation and 10 for testing. Using CT image data, planning target volumes (PTVs) and contour delineation of the organs at risk (OARs) as input and three-dimensional (3D) dose distribution as output, a 3D-Uet DL model was developed. Based on the voxel-wise prediction dose distribution, intensity-modulated radiation therapy (IMRT) plans were then generated automatedly using post-optimization strategies, including a complex clinical dose target metrics homogeneity index (HI) and conformation index (CI). To evaluate the performance of the proposed ATP approach, the dose-volume histogram (DVH) parameters of OARs and PTV and the 3D dose distributions of the plan were compared with those of manual plans. Results By combining clinical post-optimization strategies, the automatically generated treatment plan can achieve better homogeneous PTV coverage and dose sparing for OARs except the mean dose for femoral-head compared with the use of the mean square error objective function alone. Compared with the manual plan, no statistically significant differences in HI, CI or global maximum dose were found. The manual plans perform slightly better than plans with post-optimization strategies in other dosimetric indexes, but these plans are still within clinical requirements. Conclusions With the help of clinical post-optimization strategies, the proposed new ATP solution can generate IMRT plans that are within clinically acceptable levels and comparable to plans manually generated by dosimetrists.
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Affiliation(s)
- Yang Zhong
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Lei Yu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Jun Zhao
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Yingtao Fang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Yanju Yang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Zhiqiang Wu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Jiazhou Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Weigang Hu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
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Cilla S, Romano C, Morabito VE, Macchia G, Buwenge M, Dinapoli N, Indovina L, Strigari L, Morganti AG, Valentini V, Deodato F. Personalized Treatment Planning Automation in Prostate Cancer Radiation Oncology: A Comprehensive Dosimetric Study. Front Oncol 2021; 11:636529. [PMID: 34141608 PMCID: PMC8204695 DOI: 10.3389/fonc.2021.636529] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/24/2021] [Indexed: 01/08/2023] Open
Abstract
Background In radiation oncology, automation of treatment planning has reported the potential to improve plan quality and increase planning efficiency. We performed a comprehensive dosimetric evaluation of the new Personalized algorithm implemented in Pinnacle3 for full planning automation of VMAT prostate cancer treatments. Material and Methods Thirteen low-risk prostate (without lymph-nodes irradiation) and 13 high-risk prostate (with lymph-nodes irradiation) treatments were retrospectively taken from our clinical database and re-optimized using two different automated engines implemented in the Pinnacle treatment system. These two automated engines, the currently used Autoplanning and the new Personalized are both template-based algorithms that use a wish-list to formulate the planning goals and an iterative approach able to mimic the planning procedure usually adopted by experienced planners. In addition, the new Personalized module integrates a new engine, the Feasibility module, able to generate an “a priori” DVH prediction of the achievability of planning goals. Comparison between clinically accepted manually generated (MP) and automated plans generated with both Autoplanning (AP) and Personalized engines (Pers) were performed using dose-volume histogram metrics and conformity indexes. Three different normal tissue complication probabilities (NTCPs) models were used for rectal toxicity evaluation. The planning efficiency and the accuracy of dose delivery were assessed for all plans. Results For similar targets coverage, Pers plans reported a significant increase of dose conformity and less irradiation of healthy tissue, with significant dose reduction for rectum, bladder, and femurs. On average, Pers plans decreased rectal mean dose by 11.3 and 8.3 Gy for low-risk and high-risk cohorts, respectively. Similarly, the Pers plans decreased the bladder mean doses by 7.3 and 7.6 Gy for low-risk and high-risk cohorts, respectively. The integral dose was reduced by 11–16% with respect to MP plans. Overall planning times were dramatically reduced to about 7 and 15 min for Pers plans. Despite the increased complexity, all plans passed the 3%/2 mm γ-analysis for dose verification. Conclusions The Personalized engine provided an overall increase of plan quality, in terms of dose conformity and sparing of normal tissues for prostate cancer patients. The Feasibility “a priori” DVH prediction module provided OARs dose sparing well beyond the clinical objectives. The new Pinnacle Personalized algorithms outperformed the currently used Autoplanning ones as solution for treatment planning automation.
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Affiliation(s)
- Savino Cilla
- Medical Physics Unit, Gemelli Molise Hospital-Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Carmela Romano
- Medical Physics Unit, Gemelli Molise Hospital-Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Vittoria E Morabito
- Medical Physics Unit, Gemelli Molise Hospital-Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Gabriella Macchia
- Radiation Oncology Unit, Gemelli Molise Hospital-Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Milly Buwenge
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.,DIMES, Alma Mater Studiorum Bologna University, Bologna, Italy
| | - Nicola Dinapoli
- Radiation Oncology Department, Fondazione Policlinico Universitario A. Gemelli-Università Cattolica del Sacro Cuore, Rome, Italy
| | - Luca Indovina
- Medical Physics Unit, Fondazione Policlinico Universitario A. Gemelli-Università Cattolica del Sacro Cuore, Rome, Italy
| | - Lidia Strigari
- Medical Physics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Alessio G Morganti
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.,DIMES, Alma Mater Studiorum Bologna University, Bologna, Italy
| | - Vincenzo Valentini
- Radiation Oncology Department, Fondazione Policlinico Universitario A. Gemelli-Università Cattolica del Sacro Cuore, Rome, Italy.,Istituto di Radiologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Francesco Deodato
- Radiation Oncology Unit, Gemelli Molise Hospital-Università Cattolica del Sacro Cuore, Campobasso, Italy.,Istituto di Radiologia, Università Cattolica del Sacro Cuore, Rome, Italy
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Wada Y, Monzen H, Tamura M, Otsuka M, Inada M, Ishikawa K, Doi H, Nakamatsu K, Nishimura Y. Dosimetric Evaluation of Simplified Knowledge-Based Plan with an Extensive Stepping Validation Approach in Volumetric-Modulated Arc Therapy-Stereotactic Body Radiotherapy for Lung Cancer. J Med Phys 2021; 46:7-15. [PMID: 34267484 PMCID: PMC8240912 DOI: 10.4103/jmp.jmp_67_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 03/05/2021] [Accepted: 03/05/2021] [Indexed: 11/18/2022] Open
Abstract
Purpose: We investigated the performance of the simplified knowledge-based plans (KBPs) in stereotactic body radiotherapy (SBRT) with volumetric-modulated arc therapy (VMAT) for lung cancer. Materials and Methods: For 50 cases who underwent SBRT, only three structures were registered into knowledge-based model: total lung, spinal cord, and planning target volume. We performed single auto-optimization on VMAT plans in two steps: 19 cases used for the model training (closed-loop validation) and 16 new cases outside of training set (open-loop validation) for TrueBeam (TB) and Halcyon (Hal) linacs. The dosimetric parameters were compared between clinical plans (CLPs) and KBPs: CLPclosed, KBPclosed-TB and KBPclosed-Hal in closed-loop validation, CLPopen, KBPopen-TB and KBPopen-Hal in open-loop validation. Results: All organs at risk were comparable between CLPs and KBPs except for contralateral lung: V5 of KBPs was approximately 3%–7% higher than that of CLPs. V20 of total lung for KBPs showed comparable to CLPs; CLPclosed vs. KBPclosed-TB and CLPclosed vs. KBPclosed-Hal: 4.36% ± 2.87% vs. 3.54% ± 1.95% and 4.36 ± 2.87% vs. 3.54% ± 1.94% (P = 0.54 and 0.54); CLPopen vs. KBPopen-TB and CLPopen vs. KBPopen-Hal: 4.18% ± 1.57% vs. 3.55% ± 1.27% and 4.18% ± 1.57% vs. 3.67% ± 1.26% (P = 0.19 and 0.27). CI95 of KBPs with both linacs was superior to that of the CLP in closed-loop validation: CLPclosed vs. KBPclosed-TB vs. KBPclosed-Hal: 1.32% ± 0.12% vs. 1.18% ± 0.09% vs. 1.17% ± 0.06% (P < 0.01); and open-loop validation: CLPopen vs. KBPopen-TB vs. KBPopen-Hal: 1.22% ± 0.09% vs. 1.14% ± 0.04% vs. 1.16% ± 0.05% (P ≤ 0.01). Conclusions: The simplified KBPs with limited number of structures and without planner intervention were clinically acceptable in the dosimetric parameters for lung VMAT-SBRT planning.
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Affiliation(s)
- Yutaro Wada
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osaka, Japan
| | - Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osakasayama, Osaka, Japan
| | - Mikoto Tamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osakasayama, Osaka, Japan
| | - Masakazu Otsuka
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osakasayama, Osaka, Japan
| | - Masahiro Inada
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osaka, Japan
| | - Kazuki Ishikawa
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osaka, Japan
| | - Hiroshi Doi
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osaka, Japan
| | - Kiyoshi Nakamatsu
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osaka, Japan
| | - Yasumasa Nishimura
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osaka, Japan
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First experience of autonomous, un-supervised treatment planning integrated in adaptive MR-guided radiotherapy and delivered to a patient with prostate cancer. Radiother Oncol 2021; 159:197-201. [PMID: 33812912 DOI: 10.1016/j.radonc.2021.03.032] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/23/2021] [Accepted: 03/24/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND PURPOSE Currently clinical radiotherapy (RT) planning consists of a multi-step routine procedure requiring human interaction which often results in a time-consuming and fragmented process with limited robustness. Here we present an autonomous un-supervised treatment planning approach, integrated as basis for online adaptive magnetic resonance guided RT (MRgRT), which was delivered to a prostate cancer patient as a first-in-human experience. MATERIALS AND METHODS For an intermediate risk prostate cancer patient OARs and targets were automatically segmented using a deep learning-based software and logical volume operators. A baseline plan for the 1.5 T MR-Linac (20x3 Gy) was automatically generated using particle swarm optimization (PSO) without any human interaction. Plan quality was evaluated by predefined dosimetric criteria including appropriate tolerances. Online plan adaptation during clinical MRgRT was defined as first checkpoint for human interaction. RESULTS OARs and targets were successfully segmented (3 min) and used for automatic plan optimization (300 min). The autonomous generated plan satisfied 12/16 dosimetric criteria, however all remained within tolerance. Without prior human validation, this baseline plan was successfully used during online MRgRT plan adaptation, where 14/16 criteria were fulfilled. As postulated, human interaction was necessary only during plan adaptation. CONCLUSION Autonomous, un-supervised data preparation and treatment planning was first-in-human shown to be feasible for adaptive MRgRT and successfully applied. The checkpoint for first human intervention was at the time of online MRgRT plan adaptation. Autonomous planning reduced the time delay between simulation and start of RT and may thus allow for real-time MRgRT applications in the future.
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Wall PDH, Fontenot JD. Quality assurance-based optimization (QAO): Towards improving patient-specific quality assurance in volumetric modulated arc therapy plans using machine learning. Phys Med 2021; 87:136-143. [PMID: 33775567 DOI: 10.1016/j.ejmp.2021.03.017] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 03/01/2021] [Accepted: 03/09/2021] [Indexed: 10/21/2022] Open
Abstract
INTRODUCTION Previous literature has shown general trade-offs between plan complexity and resulting quality assurance (QA) outcomes. However, existing solutions for controlling this trade-off do not guarantee corresponding improvements in deliverability. Therefore, this work explored the feasibility of an optimization framework for directly maximizing predicted QA outcomes of plans without compromising the dosimetric quality of plans designed with an established knowledge-based planning (KBP) technique. MATERIALS AND METHODS A support vector machine (SVM) was developed - using a database of 500 previous VMAT plans - to predict gamma passing rates (GPRs; 3%/3mm percent dose-difference/distance-to-agreement with local normalization) based on selected complexity features. A heuristic, QA-based optimization (QAO) framework was devised by utilizing the SVM model to iteratively modify mechanical treatment features most commonly associated with suboptimal GPRs. Specifically, leaf gaps (LGs) <50 mm were widened by random amounts, which impacts all aperture-based complexity features. 13 prostate KBP-guided VMAT plans were optimized via QAO using user-specified maximum LG displacements before corresponding changes in predicted GPRs and dose were assessed. RESULTS Predicted GPRs increased by an average of 1.14 ± 1.25% (p = 0.006) with QAO using a 3 mm maximum random LG displacement. There were small differences in dose, resulting in similarly small changes in tumor control probability (maximum increase = 0.05%) and normal tissue complication probabilities in the bladder, rectum, and femoral heads (maximum decrease = 0.2% in the rectum). CONCLUSION This study explored the feasibility of QAO and warrants future investigations of further incorporating QA endpoints into plan optimization.
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Affiliation(s)
- Phillip D H Wall
- Department of Physics and Astronomy, Louisiana State University and Agricultural and Mechanical College, 202 Tower Drive, Baton Rouge, LA 70803-4001, USA.
| | - Jonas D Fontenot
- Department of Physics and Astronomy, Louisiana State University and Agricultural and Mechanical College, 202 Tower Drive, Baton Rouge, LA 70803-4001, USA; Department of Physics, Mary Bird Perkins Cancer Center, 4950 Essen Lane, Baton Rouge, LA 70809, USA
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Wang M, Gu H, Hu J, Liang J, Xu S, Qi Z. Evaluation of a highly refined prediction model in knowledge-based volumetric modulated arc therapy planning for cervical cancer. Radiat Oncol 2021; 16:58. [PMID: 33752699 PMCID: PMC7983216 DOI: 10.1186/s13014-021-01783-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 03/10/2021] [Indexed: 11/22/2022] Open
Abstract
Background and purpose To explore whether a highly refined dose volume histograms (DVH) prediction model can improve the accuracy and reliability of knowledge-based volumetric modulated arc therapy (VMAT) planning for cervical cancer. Methods and materials The proposed model underwent repeated refining through progressive training until the training samples increased from initial 25 prior plans up to 100 cases. The estimated DVHs derived from the prediction models of different runs of training were compared in 35 new cervical cancer patients to analyze the effect of such an interactive plan and model evolution method. The reliability and efficiency of knowledge-based planning (KBP) using this highly refined model in improving the consistency and quality of the VMAT plans were also evaluated. Results The prediction ability was reinforced with the increased number of refinements in terms of normal tissue sparing. With enhanced prediction accuracy, more than 60% of automatic plan-6 (AP-6) plans (22/35) can be directly approved for clinical treatment without any manual revision. The plan quality scores for clinically approved plans (CPs) and manual plans (MPs) were on average 89.02 ± 4.83 and 86.48 ± 3.92 (p < 0.001). Knowledge-based planning significantly reduced the Dmean and V18 Gy for kidney (L/R), the Dmean, V30 Gy, and V40 Gy for bladder, rectum, and femoral head (L/R). Conclusion The proposed model evolution method provides a practical way for the KBP to enhance its prediction ability with minimal human intervene. This highly refined prediction model can better guide KBP in improving the consistency and quality of the VMAT plans.
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Affiliation(s)
- Mingli Wang
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.,Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, People's Republic of China
| | - Huikuan Gu
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.,Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, People's Republic of China
| | - Jiang Hu
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.,Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, People's Republic of China
| | - Jian Liang
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.,Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, People's Republic of China
| | - Sisi Xu
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.,Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, People's Republic of China
| | - Zhenyu Qi
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China. .,State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China. .,Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, People's Republic of China.
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Inoue E, Doi H, Monzen H, Tamura M, Inada M, Ishikawa K, Nakamatsu K, Nishimura Y. Dose-volume Histogram Analysis of Knowledge-based Volumetric-modulated Arc Therapy Planning in Postoperative Breast Cancer Irradiation. In Vivo 2021; 34:1095-1101. [PMID: 32354897 DOI: 10.21873/invivo.11880] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 02/04/2020] [Accepted: 02/10/2020] [Indexed: 12/21/2022]
Abstract
BACKGROUND/AIM We evaluated the dosimetric profiles of manually generated volumetric-modulated arc therapy (VMAT) plans and performance of a commercial knowledge-based planning system (KBP) in treating breast cancer. MATERIALS AND METHODS We defined the manually generated VMAT plan as the manual plan (MP). Twenty MPs were generated for left-sided breast cancer patients who underwent breast-conserving surgery and used to develop a KBP training set. The other five patients were used for validation. The dosimetric parameters among MPs, tangential irradiation plans (TPs), and KBP-VMAT plans (KBP-Ps) were compared. RESULTS D95 and homogeneity of the planning target volume (PTV) were significantly higher and greater in MPs and KBP-Ps than in TPs. Lung V20, V40 The Dmean for the left anterior descending artery was lower in MPs and KBP-Ps than in TPs. KBP could save time in generating VMAT plans. CONCLUSION MPs and KBP-Ps could ensure higher dose uniformity of PTV than TPs. KBP could faster generate comparable MPs for breast cancer.
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Affiliation(s)
- Eri Inoue
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Hiroshi Doi
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osaka, Japan
| | - Mikoto Tamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osaka, Japan
| | - Masahiro Inada
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Kazuki Ishikawa
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Kiyoshi Nakamatsu
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Yasumasa Nishimura
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osaka, Japan
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Ito T, Tamura M, Monzen H, Matsumoto K, Nakamatsu K, Harada T, Fukui T. [Impact of Aperture Shape Controller on Knowledge-based VMAT Planning of Prostate Cancer]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2021; 77:23-31. [PMID: 33473076 DOI: 10.6009/jjrt.2021_jsrt_77.1.23] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
PURPOSE Knowledge-based planning (KBP) has disadvantages of high monitor unit (MU) and complex multi-leaf collimator (MLC) motion. We investigated the optimal aperture shape controller (ASC) level for the KBP to reduce these factors in volumetric modulated arc therapy (VMAT) for prostate cancer. METHODS The KBP model was created based on 51 clinical plans (CPs) of patients who underwent the VMAT for prostate cancer. Another 10 CPs were selected randomly, and the KBPs with/without ASC, changed stepwise from very low (KBP-VL) to very high (KBP-VH), were performed with a single auto-optimization. The parameters of dose-volume histograms (DVHs) and MLC performance metrics were evaluated. We obtained the modulation complexity score for VMAT (MCSv), closed leaf score (CLS), small aperture score (SAS), leaf travel (LT), and total MU. RESULTS The ASC did not affect the DVH parameters negatively. The following comparisons of MLC performance were obtained (KBP vs. KBP-VL vs. KBP-VH, respectively): 0.25 vs. 0.27 vs. 0.30 (MCSv), 0.19 vs. 0.18 vs. 0.16 (CLS), 0.50 vs. 0.45 vs. 0.40 (SAS10 mm), 0.73 vs. 0.68 vs. 0.63 (SAS20 mm), 768.35 mm vs. 671.50 mm vs. 551.32 mm (LT), and 672.87 vs. 642.36 vs. 607.59 (MU). There were significant differences between KBP and KBP-VH for MCSv and LT (p<0.05). CONCLUSIONS The KBP using an ASC set to the very high level could reduce the complexity of MLC motion significantly more without deterioration of the DVH parameters compared with the KBP in VMAT for prostate cancer.
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Affiliation(s)
- Takaaki Ito
- Department of Radiological Technology, Kobe City Nishi-Kobe Medical Center
| | - Mikoto Tamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University
| | - Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University
| | - Kenji Matsumoto
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University.,Department of Radiology, Kindai University Hospital
| | - Kiyoshi Nakamatsu
- Department of Radiation Oncology, Faculty of Medicine, Kindai University
| | - Tomoko Harada
- Department of Radiological Technology, Kobe City Nishi-Kobe Medical Center
| | - Tatsuya Fukui
- Department of Radiological Technology, Kobe City Nishi-Kobe Medical Center
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Rago M, Placidi L, Polsoni M, Rambaldi G, Cusumano D, Greco F, Indovina L, Menna S, Placidi E, Stimato G, Teodoli S, Mattiucci GC, Chiesa S, Marazzi F, Masiello V, Valentini V, De Spirito M, Azario L. Evaluation of a generalized knowledge-based planning performance for VMAT irradiation of breast and locoregional lymph nodes-Internal mammary and/or supraclavicular regions. PLoS One 2021; 16:e0245305. [PMID: 33449952 PMCID: PMC7810311 DOI: 10.1371/journal.pone.0245305] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 12/24/2020] [Indexed: 11/29/2022] Open
Abstract
PURPOSE To evaluate the performance of eleven Knowledge-Based (KB) models for planning optimization (RapidPlantm (RP), Varian) of Volumetric Modulated Arc Therapy (VMAT) applied to whole breast comprehensive of nodal stations, internal mammary and/or supraclavicular regions. METHODS AND MATERIALS Six RP models have been generated and trained based on 120 VMAT plans data set with different criteria. Two extra-structures were delineated: a PTV for the optimization and a ring structure. Five more models, twins of the previous models, have been created without the need of these structures. RESULTS All models were successfully validated on an independent cohort of 40 patients, 30 from the same institute that provided the training patients and 10 from an additional institute, with the resulting plans being of equal or better quality compared with the clinical plans. The internal validation shows that the models reduce the heart maximum dose of about 2 Gy, the mean dose of about 1 Gy and the V20Gy of 1.5 Gy on average. Model R and L together with model B without optimization structures ensured the best outcomes in the 20% of the values compared to other models. The external validation observed an average improvement of at least 16% for the V5Gy of lungs in RP plans. The mean heart dose and for the V20Gy for lung IPSI were almost halved. The models reduce the maximum dose for the spinal canal of more than 2 Gy on average. CONCLUSIONS All KB models allow a homogeneous plan quality and some dosimetric gains, as we saw in both internal and external validation. Sub-KB models, developed by splitting right and left breast cases or including only whole breast with locoregional lymph nodes, have shown good performances, comparable but slightly worse than the general model. Finally, models generated without the optimization structures, performed better than the original ones.
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Affiliation(s)
- Maria Rago
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Lorenzo Placidi
- Università Cattolica del Sacro Cuore, Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Mattia Polsoni
- Fatebenefratelli Isola Tiberina, Ospedale San Giovanni Calibita, Rome, Italy
- Amethyst Radioterapia Italia, Isola Tiberina, Rome, Italy
| | - Giulia Rambaldi
- Fatebenefratelli Isola Tiberina, Ospedale San Giovanni Calibita, Rome, Italy
- Amethyst Radioterapia Italia, Isola Tiberina, Rome, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Francesca Greco
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Luca Indovina
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Sebastiano Menna
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Elisa Placidi
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | | | - Stefania Teodoli
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | | | - Silvia Chiesa
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Fabio Marazzi
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Valeria Masiello
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Vincenzo Valentini
- Università Cattolica del Sacro Cuore, Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Marco De Spirito
- Università Cattolica del Sacro Cuore, Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Luigi Azario
- Università Cattolica del Sacro Cuore, Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
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Visak J, Ge GY, McGarry RC, Randall M, Pokhrel D. An Automated knowledge-based planning routine for stereotactic body radiotherapy of peripheral lung tumors via DCA-based volumetric modulated arc therapy. J Appl Clin Med Phys 2020; 22:109-116. [PMID: 33270975 PMCID: PMC7856484 DOI: 10.1002/acm2.13114] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/20/2020] [Accepted: 11/09/2020] [Indexed: 12/14/2022] Open
Abstract
Purpose To develop a knowledge‐based planning (KBP) routine for stereotactic body radiotherapy (SBRT) of peripherally located early‐stage non‐small‐cell lung cancer (NSCLC) tumors via dynamic conformal arc (DCA)‐based volumetric modulated arc therapy (VMAT) using the commercially available RapidPlanTM software. This proposed technique potentially improves plan quality, reduces complexity, and minimizes interplay effect and small‐field dosimetry errors associated with treatment delivery. Methods KBP model was developed and validated using 70 clinically treated high quality non‐coplanar VMAT lung SBRT plans for training and 20 independent plans for validation. All patients were treated with 54 Gy in three treatments. Additionally, a novel k‐DCA planning routine was deployed to create plans incorporating historical three‐dimensional‐conformal SBRT planning practices via DCA‐based approach prior to VMAT optimization in an automated planning engine. Conventional KBPs and k‐DCA plans were compared with clinically treated plans per RTOG‐0618 requirements for target conformity, tumor dose heterogeneity, intermediate dose fall‐off and organs‐at‐risk (OAR) sparing. Treatment planning time, treatment delivery efficiency, and accuracy were recorded. Results KBPs and k‐DCA plans were similar or better than clinical plans. Average planning target volume for validation was 22.4 ± 14.1 cc (7.1–62.3 cc). KBPs and k‐DCA plans provided similar conformity to clinical plans with average absolute differences of 0.01 and 0.01, respectively. Maximal doses to OAR were lowered in both KBPs and k‐DCA plans. KBPs increased monitor units (MU) on average 1316 (P < 0.001) while k‐DCA reduced total MU on average by 1114 (P < 0.001). This routine can create k‐DCA plan in less than 30 min. Independent Monte Carlo calculation demonstrated that k‐DCA plans showed better agreement with planned dose distribution. Conclusion A k‐DCA planning routine was developed in concurrence with a knowledge‐based approach for the treatment of peripherally located lung tumors. This method minimizes plan complexity associated with model‐based KBP techniques and improve plan quality and treatment planning efficiency.
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Affiliation(s)
- Justin Visak
- Medical Physics Graduate Program, Department of Radiation Medicine, University Kentucky, Lexington, KY, USA
| | - Gary Y Ge
- Medical Physics Graduate Program, Department of Radiation Medicine, University Kentucky, Lexington, KY, USA
| | - Ronald C McGarry
- Medical Physics Graduate Program, Department of Radiation Medicine, University Kentucky, Lexington, KY, USA
| | - Marcus Randall
- Medical Physics Graduate Program, Department of Radiation Medicine, University Kentucky, Lexington, KY, USA
| | - Damodar Pokhrel
- Medical Physics Graduate Program, Department of Radiation Medicine, University Kentucky, Lexington, KY, USA
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van Gysen K, O'Toole J, Le A, Wu K, Schuler T, Porter B, Kipritidis J, Atyeo J, Brown C, Eade T. Rolling out RapidPlan: What we've learnt. J Med Radiat Sci 2020; 67:310-317. [PMID: 32881407 PMCID: PMC7754012 DOI: 10.1002/jmrs.420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 07/16/2020] [Indexed: 11/11/2022] Open
Abstract
INTRODUCTION RapidPlan (RP), a knowledge-based planning system, aims to consistently improve plan quality and efficiency in radiotherapy. During the early stages of implementation, some of the challenges include knowing how to optimally train a model and how to integrate RP into a department. We discuss our experience with the implementation of RP into our institution. METHODS We reviewed all patients planned using RP over a 7-month period following inception in our department. Our primary outcome was clinically acceptable plans (used for treatment) with secondary outcomes including model performance and a comparison of efficiency and plan quality between RP and manual planning (MP). RESULTS Between November 2017 and May 2018, 496 patients were simulated, of which 217 (43.8%) had an available model. RP successfully created a clinically acceptable plan in 87.2% of eligible patients. The individual success of the 24 models ranged from 50% to 100%, with more than 90% success in 15 (62.5%) of the models. In 40% of plans, success was achieved on the 1st optimisation. The overall planning time with RP was reduced by up to 95% compared with MP times. The quality of the RP plans was at least equivalent to historical MP plans in terms of target coverage and organ at risk constraints. CONCLUSION While initially time-consuming and resource-intensive to implement, plans optimised with RP demonstrate clinically acceptable plan quality, while significantly improving the efficiency of a department, suggesting RP and its application is a highly effective tool in clinical practice.
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Affiliation(s)
- Kirsten van Gysen
- Northern Sydney Cancer CentreRoyal North Shore HospitalSt LeonardsNSWAustralia
| | - James O'Toole
- Northern Sydney Cancer CentreRoyal North Shore HospitalSt LeonardsNSWAustralia
| | - Andrew Le
- Northern Sydney Cancer CentreRoyal North Shore HospitalSt LeonardsNSWAustralia
| | - Kenny Wu
- Northern Sydney Cancer CentreRoyal North Shore HospitalSt LeonardsNSWAustralia
| | - Thilo Schuler
- Northern Sydney Cancer CentreRoyal North Shore HospitalSt LeonardsNSWAustralia
| | - Brian Porter
- Northern Sydney Cancer CentreRoyal North Shore HospitalSt LeonardsNSWAustralia
| | - John Kipritidis
- Northern Sydney Cancer CentreRoyal North Shore HospitalSt LeonardsNSWAustralia
| | - John Atyeo
- Northern Sydney Cancer CentreRoyal North Shore HospitalSt LeonardsNSWAustralia
| | - Chris Brown
- Northern Sydney Cancer CentreRoyal North Shore HospitalSt LeonardsNSWAustralia
- NHMRC Clinical Trial CentreUniversity of SydneyCamperdownNSWAustralia
| | - Thomas Eade
- Northern Sydney Cancer CentreRoyal North Shore HospitalSt LeonardsNSWAustralia
- Northern Clinical SchoolUniversity of SydneyCamperdownNSWAustralia
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Miki K, Kusters M, Nakashima T, Saito A, Kawahara D, Nishibuchi I, Kimura T, Murakami Y, Nagata Y. Evaluation of optimization workflow using custom-made planning through predicted dose distribution for head and neck tumor treatment. Phys Med 2020; 80:167-174. [DOI: 10.1016/j.ejmp.2020.10.028] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 10/14/2020] [Accepted: 10/29/2020] [Indexed: 10/23/2022] Open
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Wang M, Zhang Q, Lam S, Cai J, Yang R. A Review on Application of Deep Learning Algorithms in External Beam Radiotherapy Automated Treatment Planning. Front Oncol 2020; 10:580919. [PMID: 33194711 PMCID: PMC7645101 DOI: 10.3389/fonc.2020.580919] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 09/16/2020] [Indexed: 01/03/2023] Open
Abstract
Treatment planning plays an important role in the process of radiotherapy (RT). The quality of the treatment plan directly and significantly affects patient treatment outcomes. In the past decades, technological advances in computer and software have promoted the development of RT treatment planning systems with sophisticated dose calculation and optimization algorithms. Treatment planners now have greater flexibility in designing highly complex RT treatment plans in order to mitigate the damage to healthy tissues better while maximizing radiation dose to tumor targets. Nevertheless, treatment planning is still largely a time-inefficient and labor-intensive process in current clinical practice. Artificial intelligence, including machine learning (ML) and deep learning (DL), has been recently used to automate RT treatment planning and has gained enormous attention in the RT community due to its great promises in improving treatment planning quality and efficiency. In this article, we reviewed the historical advancement, strengths, and weaknesses of various DL-based automated RT treatment planning techniques. We have also discussed the challenges, issues, and potential research directions of DL-based automated RT treatment planning techniques.
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Affiliation(s)
- Mingqing Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Qilin Zhang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Saikit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Ruijie Yang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
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Jihong C, Penggang B, Xiuchun Z, Kaiqiang C, Wenjuan C, Yitao D, Jiewei Q, Kerun Q, Jing Z, Tianming W. Automated Intensity Modulated Radiation Therapy Treatment Planning for Cervical Cancer Based on Convolution Neural Network. Technol Cancer Res Treat 2020; 19:1533033820957002. [PMID: 33016230 PMCID: PMC7543127 DOI: 10.1177/1533033820957002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Purpose: To develop and evaluate an automatic intensity-modulated radiation therapy (IMRT) program for cervical cancer, including a Convolution Neural Network (CNN)-based prediction model and an automated optimization strategy. Methods: A CNN deep learning model was trained to predict a patient-specify set of IMRT objectives based on overlap volume histograms (OVH) and high-quality plan of previous patients. A total of 140 cervical cancer patients were enrolled in this study, including 100 patients in the training set, 20 patients in the validation set and 20 patients in the testing set. The input of this model was OVH data and the output were values of IMRT plan objectives. For patients in the testing set, the set of planning objectives were predicted by the CNN model and used to automatically generate IMRT plans. Meanwhile, manual plans of these patients were generated by 1 beginner planner and 1 senior planner respectively. Finally, dose distribution, dosimetric parameters and planning time were analyzed. In addition, the 3 types of plans were blinded compared and ranked by an experienced oncologist. Results: There were almost no statistically differences among these 3 types of plans in target coverage and dose conformity. Dose homogeneity were slightly decreased while the average dose and parameters for most organs-at-risk (OARs) were decreased in automatic plans. Especially in comparison with manual plans by the beginner planner, V40 of bladder and rectum decreased 6.3% and 12.3%, while mean dose of rectum and marrow were 1.1 Gy and 1.8 Gy lower with automatic plans (either P < 0.017). In the blinded comparison, automatic plans were chosen as best plan in 14 cases. Conclusions: For cervical cancer, automatic IMRT plans optimized from the CNN generated objectives have superior dose sparing without compromising of target dose. It significantly reduced the planning time.
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Affiliation(s)
- Chen Jihong
- Department of Radiation Oncology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China
| | - Bai Penggang
- Department of Radiation Oncology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China
| | - Zhang Xiuchun
- Department of Radiation Oncology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China
| | - Chen Kaiqiang
- Department of Radiation Oncology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China
| | - Chen Wenjuan
- Department of Radiation Oncology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China
| | - Dai Yitao
- Department of Radiation Oncology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China
| | - Qian Jiewei
- School of Nuclear Science and Technology, University of South China, Hengyang, China
| | - Quan Kerun
- School of Nuclear Science and Technology, University of South China, Hengyang, China
| | - Zhong Jing
- Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China
| | - Wu Tianming
- Department of Radiation and Cellular Oncology, The University of Chicago Medicine, IL, USA
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Treatment plan quality assessment for radiotherapy of rectal cancer patients using prediction of organ-at-risk dose metrics. Phys Imaging Radiat Oncol 2020; 16:74-80. [PMID: 33458347 PMCID: PMC7807565 DOI: 10.1016/j.phro.2020.10.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 10/08/2020] [Accepted: 10/09/2020] [Indexed: 11/30/2022] Open
Abstract
Background and purpose Radiotherapy centers frequently lack simple tools for periodic treatment plan verification and feedback on current plan quality. It is difficult to measure treatment quality over different years or during the planning process. Here, we implemented plan quality assurance (QA) by developing a database of dose-volume histogram (DVH) metrics and a prediction model. These tools were used to assess automatically optimized treatment plans for rectal cancer patients, based on cohort analysis. Material and methods A treatment plan QA framework was established and an overlap volume histogram based model was used to predict DVH parameters for cohorts of patients treated in 2018 and 2019 and grouped according to planning technique. A training cohort of 22 re-optimized treatment plans was used to make the prediction model. The prediction model was validated on 95 automatically generated treatment plans (automatically optimized cohort) and 93 manually optimized plans (manually optimized cohort). Results For the manually optimized cohort, on average the prediction deviated less than 0.3 ± 1.4 Gy and −4.3 ± 5.5 Gy, for the mean doses to the bowel bag and bladder, respectively; for the automatically optimized cohort a smaller deviation was observed: −0.1 ± 1.1 Gy and −0.2 ± 2.5 Gy, respectively. The interquartile range of DVH parameters was on average smaller for the automatically optimized cohort, indicating less variation within each parameter compared to manual planning. Conclusion An automated framework to monitor treatment quality with a DVH prediction model was successfully implemented clinically and revealed less variation in DVH parameters for automated in comparison to manually optimized plans. The framework also allowed for individual feedback and DVH estimation.
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Monzen H, Tamura M, Ueda Y, Fukunaga JI, Kamima T, Muraki Y, Kubo K, Nakamatsu K. Dosimetric evaluation with knowledge-based planning created at different periods in volumetric-modulated arc therapy for prostate cancer: a multi-institution study. Radiol Phys Technol 2020; 13:327-335. [PMID: 32986184 DOI: 10.1007/s12194-020-00585-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 09/16/2020] [Accepted: 09/16/2020] [Indexed: 12/22/2022]
Abstract
Dosimetric evaluation and variation assessment were performed with two knowledge-based planning (KBP) models created at different periods for volumetric-modulated arc therapy (VMAT) for prostate cancer at five institutes. The first and second models (F- and S-models) for KBP were created before April 2017 and April 2019, respectively. The S-model was created using feedback plans from the F-model. Dose evaluation was compared between the two models using the same two computed tomography (CT) datasets and structures. The evaluation metrics were the dose received by 95.0% and 2.0% of the planning target volume (PTV); dose-volume parameters to the rectum and bladder as V90, V80, and V50; and monitor unit (MU). Dosimetric variation was compared by exporting estimated dose-volume histograms for each model to the Model Analytics website and assessing the organ at risk volume. There were no dosimetric differences between the two models for PTV. The V50 of the rectum in the S-model had improved compared to that of the F-model (case I: 49.3 ± 15.6 and 43.5 ± 15.2 [p = 0.08]; case II: 42.5 ± 16.9 and 36.0 ± 15.6 [p = 0.138]). The differences in other parameters were within ± 1.8% between the rectum and the bladder. The MU was slightly higher in the S-model than in the F-model, and dosimetric variation was reduced to the rectum and bladder among all the institutes. The polished S-model for KBP could be used for standardization of the plan quality and sharing of KBP models in VMAT for prostate cancer.
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Affiliation(s)
- Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, 377-2 Ohnohigashi, Osakasayama, Osaka, 589-8511, Japan.
| | - Mikoto Tamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, 377-2 Ohnohigashi, Osakasayama, Osaka, 589-8511, Japan
| | - Yoshihiro Ueda
- Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 537-8567, Japan
| | - Jun-Ichi Fukunaga
- Divisin of Radiology, Department of Medical Technology, Kyushu University Hospital, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Tatsuya Kamima
- Department of Radiation Oncology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan
| | - Yuta Muraki
- Department of Radiology, Seirei Hamamatsu General Hospital, 2-12-12 Sumiyoshi, Naka-ku, Hamamatsu, Shizuoka, 430-8558, Japan
| | - Kazuki Kubo
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, 377-2 Ohnohigashi, Osakasayama, Osaka, 589-8511, Japan
| | - Kiyoshi Nakamatsu
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, 377-2 Ohnohigashi, Osakasayama, Osaka, 589-8511, Japan
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Bohara G, Sadeghnejad Barkousaraie A, Jiang S, Nguyen D. Using deep learning to predict beam-tunable Pareto optimal dose distribution for intensity-modulated radiation therapy. Med Phys 2020; 47:3898-3912. [PMID: 32621789 PMCID: PMC7821384 DOI: 10.1002/mp.14374] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 05/19/2020] [Accepted: 06/23/2020] [Indexed: 12/20/2022] Open
Abstract
PURPOSE Many researchers have developed deep learning models for predicting clinical dose distributions and Pareto optimal dose distributions. Models for predicting Pareto optimal dose distributions have generated optimal plans in real time using anatomical structures and static beam orientations. However, Pareto optimal dose prediction for intensity-modulated radiation therapy (IMRT) prostate planning with variable beam numbers and orientations has not yet been investigated. We propose to develop a deep learning model that can predict Pareto optimal dose distributions by using any given set of beam angles, along with patient anatomy, as input to train the deep neural networks. We implement and compare two deep learning networks that predict with two different beam configuration modalities. METHODS We generated Pareto optimal plans for 70 patients with prostate cancer. We used fluence map optimization to generate 500 IMRT plans that sampled the Pareto surface for each patient, for a total of 35 000 plans. We studied and compared two different models, Models I and II. Although they both used the same anatomical structures - including the planning target volume (PTV), organs at risk (OARs), and body - these models were designed with two different methods for representing beam angles. Model I directly uses beam angles as a second input to the network as a binary vector. Model II converts the beam angles into beam doses that are conformal to the PTV. We divided the 70 patients into 54 training, 6 validation, and 10 testing patients, thus yielding 27 000 training, 3000 validation, and 5000 testing plans. Mean square loss (MSE) was taken as the loss function. We used the Adam optimizer with a default learning rate of 0.01 to optimize the network's performance. We evaluated the models' performance by comparing their predicted dose distributions with the ground truth (Pareto optimal) dose distribution, in terms of dose volume histogram (DVH) plots and evaluation metrics such as PTV D98 , D95 , D50 , D2 , Dmax , Dmean , Paddick Conformation Number, R50, and Homogeneity index. RESULTS Our deep learning models predicted voxel-level dose distributions that precisely matched the ground truth dose distributions. The DVHs generated also precisely matched the ground truth. Evaluation metrics such as PTV statistics, dose conformity, dose spillage (R50), and homogeneity index also confirmed the accuracy of PTV curves on the DVH. Quantitatively, Model I's prediction error of 0.043 (confirmation), 0.043 (homogeneity), 0.327 (R50), 2.80% (D95), 3.90% (D98), 0.6% (D50), and 1.10% (D2) was lower than that of Model II, which obtained 0.076 (confirmation), 0.058 (homogeneity), 0.626 (R50), 7.10% (D95), 6.50% (D98), 8.40% (D50), and 6.30% (D2). Model I also outperformed Model II in terms of the mean dose error and the max dose error on the PTV, bladder, rectum, left femoral head, and right femoral head. CONCLUSIONS Treatment planners who use our models will be able to use deep learning to control the trade-offs between the PTV and OAR weights, as well as the beam number and configurations in real time. Our dose prediction methods provide a stepping stone to building automatic IMRT treatment planning.
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Affiliation(s)
- Gyanendra Bohara
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Azar Sadeghnejad Barkousaraie
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Steve Jiang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75390, USA
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Ueda Y, Monzen H, Fukunaga JI, Ohira S, Tamura M, Suzuki O, Inui S, Isono M, Miyazaki M, Sumida I, Ogawa K, Teshima T. Characterization of knowledge-based volumetric modulated arc therapy plans created by three different institutions' models for prostate cancer. Rep Pract Oncol Radiother 2020; 25:1023-1028. [PMID: 33390859 DOI: 10.1016/j.rpor.2020.08.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 06/26/2020] [Accepted: 08/14/2020] [Indexed: 11/17/2022] Open
Abstract
Background The aim of this study was to clarify factors predicting the performance of knowledge-based planning (KBP) models in volume modulated arc therapy for prostate cancer in terms of sparing the organ at risk (OAR). Materials and methods In three institutions, each KBP model was trained by more than 20 library plans (LP) per model. To validate the characterization of each KBP model, 45 validation plans (VP) were calculated by the KBP system. The ratios of overlap between the OAR volume and the planning target volume (PTV) to the whole organ volume (Voverlap/Vwhole) were analyzed for each LP and VP. Regression lines between dose-volume parameters (V90, V75, and V50) and Voverlap/Vwhole were evaluated. The mean OAR dose, V90, V75, and V50 of LP did not necessarily match those of VP. Results In both the rectum and bladder, the dose-volume parameters for VP were strongly correlated with Voverlap/Vwhole at institutes A, B, and C (R > 0.74, 0.85, and 0.56, respectively). Except in the rectum at institute B, the slopes of the regression lines for LP corresponded to those for VP. For dose-volume parameters for the rectum, the ratios of slopes of the regression lines in VP to those in LP ranged 0.51-1.26. In the bladder, most ratios were less than 1.0 (mean: 0.77). Conclusion For each OAR, each model made distinct dosimetric characterizations in terms of Voverlap/Vwhole. The relationship between dose-volume parameters and Voverlap/Vwhole of OARs in LP predicts the KBP models' performance sparing OARs.
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Affiliation(s)
- Yoshihiro Ueda
- Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 537-8567, Japan
- Department of Radiation Oncology, Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita, Osaka 565-0071, Japan
| | - Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, 377-2 Onohigashi, Osakasayama, Osaka 589-8511, Japan
| | - Jun-Ichi Fukunaga
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Shingo Ohira
- Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 537-8567, Japan
| | - Mikoto Tamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, 377-2 Onohigashi, Osakasayama, Osaka 589-8511, Japan
| | - Osamu Suzuki
- Department of Radiation Oncology, Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita, Osaka 565-0071, Japan
| | - Shoki Inui
- Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 537-8567, Japan
| | - Masaru Isono
- Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 537-8567, Japan
| | - Masayoshi Miyazaki
- Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 537-8567, Japan
| | - Iori Sumida
- Department of Radiation Oncology, Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita, Osaka 565-0071, Japan
| | - Kazuhiko Ogawa
- Department of Radiation Oncology, Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita, Osaka 565-0071, Japan
| | - Teruki Teshima
- Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 537-8567, Japan
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