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All Journal Komunikasi Fisika Indonesia Medical Journal of Indonesia
Dwi Seno Kuncoro Sihono
Department Of Physics, University Of Indonesia, Depok, Indonesia
Earth & Planetary Sciences Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Medicine & Pharmacology Physics

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Random forest algorithm for precision dose prediction in brain cancer radiotherapy Luqyana Adha Azwat; Prawito Prajitno; Dwi Seno Kuncoro Sihono; Dewa Ngurah Yudhi Persada
Indonesian Physics Communication Vol 21, No 2 (2024)
Publisher : Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/jkfi.21.2.183-186

Abstract

Improving dose optimization during clinical planning using the treatment planning system for radiotherapy patients is crucial, yet executing this process can be time-consuming and reliant on the expertise of medical physicists. This research focuses on dose prediction employing machine learning for the planning target volume (PTV) and organ at risk (OAR) in cases of brain cancer treated with the volumetric modulated arc therapy planning technique. Utilizing DICOM planning data from brain cancer cases, this study utilizes extracted radiomic and dosiomic values as inputs and outputs for the research, employing a random forest algorithm model. Evaluation of the model reveals its effectiveness in predicting doses for PTV in brain cancer and OAR, with predicted homogeneity index and conformity index values of 0.14 ± 0.04 and 0.95 ± 0.01, respectively, compared to clinical values of 0.14 ± 0.13 and 0.94 ± 0.13. Thus, the random forest model demonstrates proficiency in predicting doses for brain cancer PTV and OAR, with an mean square error value of 0.017.
Bone growth evaluation in collagen-hydroxyapatite implant locations using digital radiography: an animal model Sari, Laela; Julia, Siti; Lubis, Lukmanda Evan; Sihono, Dwi Seno Kuncoro; Sari, Yessie Widya; Soejoko, Djarwani Soeharso
Medical Journal of Indonesia Vol. 32 No. 4 (2023): December
Publisher : Faculty of Medicine Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13181/mji.oa.237051

Abstract

BACKGROUND Digital radiography has been used to evaluate the progress of bone growth with a collagen-hydroxyapatite implant in rabbit tibias. This study aimed to introduce digital radiography methods that provide comprehensive data availability for continuous information retrieval from the implant preparation to the cultivation period. METHODS 38 digital radiographs were divided into 3 treatment groups, namely a single defect without implant (control), single-implant, and three-implant. Radiographic acquisitions were performed at preparation time and post-implantation from 0 to 56 days. Observations were concentrated on the implantation site, followed by creating a lateral profile. The prediction of implantation growth was determined using relative bone density (RBD) percentage. RESULTS Based on the profile, the recovery process consisted of implant absorption and new bone tissue deposition. The absorption process was highly influenced by the defect size. In the control and single-implant groups, regardless of the different recovery processes, similar recovery results were observed 56 days post-implantation, with an RBD value of approximately 90%. Meanwhile, the three-implant group only had an RBD value of 62%. CONCLUSIONS Radiography can evaluate absorption and new bone growth during implantation in New Zealand white rabbits. Radiographs, which can be obtained at any time during cultivation, offered more information on the recovery implantation process than the other method that relies on data obtained after sacrificing the animals.
Co-Authors Dewa Ngurah Yudhi Persada Djarwani Soeharso Soejoko Julia, Siti laela Sari, laela Lubis, Lukmanda Evan Luqyana Adha Azwat Prawito Prajitno Yessie Widya Sari