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All Journal Jurnal Kybernan Jurnal Perpustakaan Pertanian Journal of Islamic Pharmacy Integritas: Jurnal Antikorupsi DoubleClick : Journal of Computer and Information Technology JURNAL PETIK Jurnal Informasi dan Komputer JASa (Jurnal Akuntansi, Audit dan Sistem Informasi Akuntansi) Negara Hukum: Membangun Hukum untuk Keadilan dan Kesejahteraan ADLIYA: Jurnal Hukum dan Kemanusiaan Jurnal Loyalitas Sosial: Journal of Community Service in Humanities and Social Sciences Altasia : Jurnal Pariwisata Indonesia Tematik : Jurnal Teknologi Informasi Komunikasi ATRABIS: Jurnal Administrasi Bisnis (e-Journal) Jurnal Health Sains Journey : Journal of Tourismpreneurship, Culinary, Hospitality, Convention and Event Management Shar-E: Jurnal Kajian Ekonomi Hukum Syariah Indonesia Private Law Review Jurnal Alwatzikhoebillah : Kajian Islam, Pendidikan, Ekonomi, Humaniora Jurnal Pengabdian Masyarakat Indonesia Jurnal Politica Dinamika Masalah Politik Dalam Negeri dan Hubungan Internasional Jurnal Teknologi Sistem Informasi Joutica : Journal of Informatic Unisla Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Indonesian Journal of Cancer CoMPHI Journal : Community Medicine and Public Health of Indonesia Journal Jurnal Hukum dan Peradilan Digital Transformation Technology (Digitech) Libraria: Ilmu Perpustakaan dan Informasi Junior Medical Journal Journal of Business and Political Economy: Biannual Review of The Indonesian Economy Review Propeller The Prosecutor Law Review Jurnal Kesehatan Kartika Jurnal Keadilan Pemilu Sulolipu: Media Komunikasi Sivitas Akademika dan Masyarakat Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat CEFARS : JURNAL AGRIBISNIS DAN PENGEMBANGAN WILAYAH Jurnal Agribisnis dan Pengembangan Wilayah Berkala Ilmiah Kedokteran dan Kesehatan Masyarakat
Rita Komalasari
Perpustakaan Institut Pertanian Bogor Jalan Meranti, IPB Kampus Dramaga, Bogor 16681 Telp. (0251) 8621073, Faks. (0251) 8623166
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Journal : Indonesian Journal of Cancer

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Optimizing Prostate Cancer Radiotherapy: Advanced Machine Learning in Virtual Patient-Specific Plan Verification-Review Study Komalasari, Rita
Indonesian Journal of Cancer Vol 18, No 3 (2024): September
Publisher : http://dharmais.co.id/

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33371/ijoc.v18i3.1128

Abstract

Background: Virtual patient-specific plan verification in radiotherapy is critical to ensure precise dose delivery while minimizing healthy tissue exposure, particularly in prostate cancer treatments. Prior studies, however, have overlooked the physical implications of predictor features and lacked comprehensive decision support tools, leading to gaps in understanding and practical application. This research aims to bridge these gaps by providing a nuanced understanding of predictor features, exploring advanced automatic feature extraction methods, emphasizing model reliability, and proposing a comprehensive decision support tool. Our objective is to optimize radiotherapy protocols, ensuring safer and more effective treatments for patients undergoing prostate cancer therapy. Methods: This research employs a literature review approach. An extensive literature study served as the foundation. Results: Our study reveals that understanding the physical implications of predictor features significantly enhances prediction accuracy. Utilizing Convolutional Neural Networks (CNN) models for automatic feature extraction improves prediction performance, providing robust and transferable results. Conclusions: By emphasizing model reliability through the integration of treatment plan parameters, our approach ensures stable predictions across diverse patient cases. The proposed decision support tool offers clinicians detailed insights into predicted dose deliverability, facilitating informed decision-making for patient-specific treatment plans. Through these advancements, our research contributes to the optimization of radiotherapy protocols, ensuring safer and more effective treatments for patients undergoing prostate cancer therapy.
Co-Authors Adilah Rifat Hakimah Adriza, Adriza Ahmad Nawawi Andina Dwijayanti Budi Harto Budiman, Diaz Viera Bukhari, Bilal Fiqri Cahyadi Dandun Widhiantoro Deyang Maysa Dhea Putri Ardita Evry, Nur Isnaeni Falahah . Faturizky, Bintang Ramadhana Hadi Putri, Camila Irianti Helmi Fahmi Fauzi Hidayat, Vasha Annisa Hilmi, Arif iswanto Laila Felicia Lucy Maulidya Sari, Siti Maulidya, Siti Melinda, Linda Daniati Mustafa, Cecep Novianti Indah Putri Nurazizah, Fahira Pramesti, Puji Rani Rantika Raysha Damatri Rasyad Roro Santi Saifullah, Razid Alfa Saputri, Rya Ervita Rachma Siti Helmyati Siti Maulidya Sari Sriharyati, Santy Sulaswati, Dinni Sumarni, Teti Suryani, Ai Tiya Yulia Fauziyah Yusnita Zen Munawar