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Araujo AS, Silva RMV, Souza DN. Evaluation of conventional IMRT and VMAT strategies for postmastectomy radiation therapy after immediate implant-based reconstruction using the new ESTRO-ACROP contouring guidelines. Radiat Environ Biophys 2024; 63:59-70. [PMID: 38300284 DOI: 10.1007/s00411-024-01059-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 01/03/2024] [Indexed: 02/02/2024]
Abstract
This study evaluated the usability of conventional templates based on the new contour guidelines of the European Society of Radiation and Oncology and Advisory Committee in Radiation Oncology Practice (ESTRO-ACROP) for treatment plans of postmastectomy radiotherapy after immediate implant-based reconstruction. Intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) plans generated with two different treatment planning systems (TPSs, Eclipse and Monaco) were examined. Six computed tomography scans of patients aged 35-54 years were retrospectively analysed who had undergone mastectomy and breast reconstruction using silicone implants after being diagnosed with left breast cancer. Six radiation oncologists participated in this study, and each of them contoured the target volume of one left breast using conventional contour (CTV-CONV) and new contour (CTV-ESTRO) methods. This study showed that compared with CTV-CONV, using CTV-ESTRO with objectives and cost functions similar to those of TPSs worsened the target volume coverage and increased the total number of monitor units. Considering the organs at risk, CTV-ESTRO tended to increase the mean dose delivered to the contralateral lung. It is concluded that the approach used for the new ESTRO-ACROP contour method cannot be applied in a manner similar to that for the conventional breast contour method, implying that the new ESTRO-ACROP contour method may require more time for improving plans for a given treatment.
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Affiliation(s)
- Andreyson S Araujo
- Departamento de Física, Universidade Federal de Sergipe, São Cristóvão, SE, 49100-000, Brazil
| | | | - Divanizia N Souza
- Departamento de Física, Universidade Federal de Sergipe, São Cristóvão, SE, 49100-000, Brazil.
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Kawata Y, Arimura H, Ikushima K, Jin Z, Morita K, Tokunaga C, Yabu-Uchi H, Shioyama Y, Sasaki T, Honda H, Sasaki M. Impact of pixel-based machine-learning techniques on automated frameworks for delineation of gross tumor volume regions for stereotactic body radiation therapy. Phys Med 2017; 42:141-149. [PMID: 29173908 DOI: 10.1016/j.ejmp.2017.08.012] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 08/21/2017] [Accepted: 08/26/2017] [Indexed: 01/03/2023] Open
Abstract
The aim of this study was to investigate the impact of pixel-based machine learning (ML) techniques, i.e., fuzzy-c-means clustering method (FCM), and the artificial neural network (ANN) and support vector machine (SVM), on an automated framework for delineation of gross tumor volume (GTV) regions of lung cancer for stereotactic body radiation therapy. The morphological and metabolic features for GTV regions, which were determined based on the knowledge of radiation oncologists, were fed on a pixel-by-pixel basis into the respective FCM, ANN, and SVM ML techniques. Then, the ML techniques were incorporated into the automated delineation framework of GTVs followed by an optimum contour selection (OCS) method, which we proposed in a previous study. The three-ML-based frameworks were evaluated for 16 lung cancer cases (six solid, four ground glass opacity (GGO), six part-solid GGO) with the datasets of planning computed tomography (CT) and 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT images using the three-dimensional Dice similarity coefficient (DSC). DSC denotes the degree of region similarity between the GTVs contoured by radiation oncologists and those estimated using the automated framework. The FCM-based framework achieved the highest DSCs of 0.79±0.06, whereas DSCs of the ANN-based and SVM-based frameworks were 0.76±0.14 and 0.73±0.14, respectively. The FCM-based framework provided the highest segmentation accuracy and precision without a learning process (lowest calculation cost). Therefore, the FCM-based framework can be useful for delineation of tumor regions in practical treatment planning.
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Affiliation(s)
- Yasuo Kawata
- Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Hidetaka Arimura
- Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
| | - Koujirou Ikushima
- Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Ze Jin
- Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Kento Morita
- Department of Health Sciences, School of Medicine, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Chiaki Tokunaga
- Department of Medical Technology, Kyushu University Hospital, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Hidetake Yabu-Uchi
- Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Yoshiyuki Shioyama
- Saga Heavy Ion Medical Accelerator in Tosu, 415, Harakoga-cho, Tosu 841-0071, Japan
| | - Tomonari Sasaki
- Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Hiroshi Honda
- Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Masayuki Sasaki
- Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
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