Literature DB >> 31982788

Knowledge-based automatic optimization of adaptive early-regression-guided VMAT for rectal cancer.

Roberta Castriconi1, Claudio Fiorino2, Paolo Passoni3, Sara Broggi1, Nadia G Di Muzio3, Giovanni M Cattaneo1, Riccardo Calandrino1.   

Abstract

PURPOSE: To implement a knowledge-based (KB) optimization strategy to our adaptive (ART) early-regression guided boosting technique in neo-adjuvant radio-chemotherapy for rectal cancer.
MATERIAL AND METHODS: The protocol consists of a first phase delivering 27.6 Gy to tumor/lymph-nodes (2.3 Gy/fr-PTV1), followed by the ART phase concomitantly delivering 18.6 Gy (3.1 Gy/fr) and 13.8 Gy (2.3 Gy/fr) to the residual tumor (PTVART) and to PTV1 respectively. PTVART is obtained by expanding the residual GTV, as visible on MRI at fraction 9. Forty plans were used to generate a KB-model for the first phase using the RapidPlan tool. Instead of building a new model, a robust strategy scaling the KB-model to the ART phase was applied. Both internal and external validation were performed for both phases: all automatic plans (RP) were compared in terms of OARs/PTVs parameters against the original plans (RA).
RESULTS: The resulting automatic plans were generally better than or equivalent to clinical plans. Of note, V30Gy and V40Gy were significantly improved in RP plans for bladder and bowel; gEUD analysis showed improvement for KB-modality for all OARs, up to 3 Gy for the bowel.
CONCLUSIONS: The KB-model generated for the first phase was robust and it was also efficiently adapted to the ART phase. The performance of automatically generated plans were slightly better than the corresponding manual plans for both phases.
Copyright © 2020 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Adaptive radiotherapy; Automatic planning; Knowledge-based optimization; Rectal cancer

Mesh:

Year:  2020        PMID: 31982788     DOI: 10.1016/j.ejmp.2020.01.016

Source DB:  PubMed          Journal:  Phys Med        ISSN: 1120-1797            Impact factor:   2.685


  6 in total

1.  Artificial Intelligence in Radiation Therapy.

Authors:  Yabo Fu; Hao Zhang; Eric D Morris; Carri K Glide-Hurst; Suraj Pai; Alberto Traverso; Leonard Wee; Ibrahim Hadzic; Per-Ivar Lønne; Chenyang Shen; Tian Liu; Xiaofeng Yang
Journal:  IEEE Trans Radiat Plasma Med Sci       Date:  2021-08-24

2.  Quantitative Comparison of Knowledge-Based and Manual Intensity Modulated Radiation Therapy Planning for Nasopharyngeal Carcinoma.

Authors:  Jiang Hu; Boji Liu; Weihao Xie; Jinhan Zhu; Xiaoli Yu; Huikuan Gu; Mingli Wang; Yixuan Wang; ZhenYu Qi
Journal:  Front Oncol       Date:  2021-01-07       Impact factor: 6.244

3.  Treatment plan quality assessment for radiotherapy of rectal cancer patients using prediction of organ-at-risk dose metrics.

Authors:  Ana Vaniqui; Richard Canters; Femke Vaassen; Colien Hazelaar; Indra Lubken; Kirsten Kremer; Cecile Wolfs; Wouter van Elmpt
Journal:  Phys Imaging Radiat Oncol       Date:  2020-10-19

4.  Knowledge-based planning using pseudo-structures for volumetric modulated arc therapy (VMAT) of postoperative uterine cervical cancer: a multi-institutional study.

Authors:  Tatsuya Kamima; Yoshihiro Ueda; Jun-Ichi Fukunaga; Mikoto Tamura; Yumiko Shimizu; Yuta Muraki; Yasuo Yoshioka; Nozomi Kitamura; Yuya Nitta; Masakazu Otsuka; Hajime Monzen
Journal:  Rep Pract Oncol Radiother       Date:  2021-12-30

5.  Replacing Manual Planning of Whole Breast Irradiation With Knowledge-Based Automatic Optimization by Virtual Tangential-Fields Arc Therapy.

Authors:  Roberta Castriconi; Pier Giorgio Esposito; Alessia Tudda; Paola Mangili; Sara Broggi; Andrei Fodor; Chiara L Deantoni; Barbara Longobardi; Marcella Pasetti; Lucia Perna; Antonella Del Vecchio; Nadia Gisella Di Muzio; Claudio Fiorino
Journal:  Front Oncol       Date:  2021-08-24       Impact factor: 6.244

6.  Reducing variability among treatment machines using knowledge-based planning for head and neck, pancreatic, and rectal cancer.

Authors:  Hideaki Hirashima; Mitsuhiro Nakamura; Nobutaka Mukumoto; Ryo Ashida; Kota Fujii; Kiyonao Nakamura; Aya Nakajima; Katsuyuki Sakanaka; Michio Yoshimura; Takashi Mizowaki
Journal:  J Appl Clin Med Phys       Date:  2021-06-20       Impact factor: 2.102

  6 in total

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