Acute toxicity prediction after breast radiotherapy using machine-learning and spectrophotometry

被引：0

作者：

Cilla, S. ^{[1
]}

Romano, C. ^{[1
]}

Macchia, G. ^{[2
]}

Boccardi, M. ^{[2
]}

Pezzulla, D. ^{[2
]}

Buwenge, M. ^{[3
]}

Di Castelnuovo, A. ^{[4
]}

Bracone, F. ^{[5
]}

De Curtis, A. ^{[5
]}

Cerletti, C. ^{[5
]}

Iacoviello, L. ^{[6
]}

Donati, M. B. ^{[5
]}

Deodato, F. ^{[2
]}

Morganti, A. G. ^{[7
]}

机构：

[1] Univ Cattolica Sacro Cuore, Med Phys Unit, Gemelli Molise Hosp, Campobasso, Italy

[2] Univ Cattolica Sacro Cuore, Radiat Oncol Unit, Gemelli Molise Hosp, Campobasso, Italy

[3] IRCCS Azienda Osped Univ Bologna, Radiat Oncol, Bologna, Italy

[4] Mediterranea Cardioctr, Dept Epidemiol & Prevent, Naples, Italy

[5] IRCCS NEUROMED, Dept Epidemiol & Prevent, Pozzilli, Italy

[6] Univ Insubria, Dept Med & Surg, EPIMED Res Ctr, Varese, Italy

[7] Bologna Univ, Dept Expt Diagnost & Specialty Med DIMES, Alma Mater Studiorum, Bologna, Italy

来源：

RADIOTHERAPY AND ONCOLOGY | 2023年 / 182卷

关键词：

D O I：

暂无

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

PO-2097

引用

页码：S1880 / S1881

页数：2

共 50 条

[31] Prediction of Dynamic Toxicity of Nanoparticles Using Machine Learning
Khokhlov, Ivan
Legashev, Leonid
Bolodurina, Irina
Shukhman, Alexander
Shoshin, Daniil
Kolesnik, Svetlana
TOXICS, 2024, 12 (10)
[32] Toxicity prediction of nanoparticles using machine learning approaches
Ahmadi, Mahnaz
Ayyoubzadeh, Seyed Mohammad
Ghorbani-Bidkorpeh, Fatemeh
TOXICOLOGY, 2024, 501
[33] Prediction of bacterial associations with plants using a supervised machine-learning approach
Manuel Martinez-Garcia, Pedro
Lopez-Solanilla, Emilia
Ramos, Cayo
Rodriguez-Palenzuela, Pablo
ENVIRONMENTAL MICROBIOLOGY, 2016, 18 (12) : 4847 - 4861
[34] Statistical Machine-Learning Methods for Genomic Prediction Using the SKM Library
Montesinos Lopez, Osval A.
Mosqueda Gonzalez, Brandon Alejandro
Montesinos Lopez, Abelardo
Crossa, Jose
GENES, 2023, 14 (05)
[35] Prediction of Post-Intubation Tachycardia Using Machine-Learning Models
Kim, Hanna
Jeong, Young-Seob
Kang, Ah Reum
Jung, Woohyun
Chung, Yang Hoon
Koo, Bon Sung
Kim, Sang Hyun
APPLIED SCIENCES-BASEL, 2020, 10 (03):
[36] Personalized risk prediction of symptomatic intracerebral hemorrhage after stroke thrombolysis using a machine-learning model
Wang, Feng
Huang, Yuanhanqing
Xia, Yong
Zhang, Wei
Fang, Kun
Zhou, Xiaoyu
Yu, Xiaofei
Cheng, Xin
Li, Gang
Wang, Xiaoping
Luo, Guojun
Wu, Danhong
Liu, Xueyuan
Campbell, Bruce C. V.
Dong, Qiang
Zhao, Yuwu
THERAPEUTIC ADVANCES IN NEUROLOGICAL DISORDERS, 2020, 13
[37] Advancing interpretability of machine-learning prediction models
Trenary, Laurie
DelSole, Timothy
ENVIRONMENTAL DATA SCIENCE, 2022, 1
[38] Anxiety onset in adolescents: a machine-learning prediction
Alice V. Chavanne
Marie Laure Paillère Martinot
Jani Penttilä
Yvonne Grimmer
Patricia Conrod
Argyris Stringaris
Betteke van Noort
Corinna Isensee
Andreas Becker
Tobias Banaschewski
Arun L. W. Bokde
Sylvane Desrivières
Herta Flor
Antoine Grigis
Hugh Garavan
Penny Gowland
Andreas Heinz
Rüdiger Brühl
Frauke Nees
Dimitri Papadopoulos Orfanos
Tomáš Paus
Luise Poustka
Sarah Hohmann
Sabina Millenet
Juliane H. Fröhner
Michael N. Smolka
Henrik Walter
Robert Whelan
Gunter Schumann
Jean-Luc Martinot
Eric Artiges
Molecular Psychiatry, 2023, 28 : 639 - 646
[39] A machine-learning algorithm for wind gust prediction
Sallis, P. J.
Claster, W.
Hernandez, S.
COMPUTERS & GEOSCIENCES, 2011, 37 (09) : 1337 - 1344
[40] Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective
Blank, Celine
Wildeboer, Rogier Rudolf
DeCroo, Ilse
Tilleman, Kelly
Weyers, Basiel
de Sutter, Petra
Mischi, Massimo
Schoot, Benedictus Christiaan
FERTILITY AND STERILITY, 2019, 111 (02) : 318 - 326

← 1 2 3 4 5 →