Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.
locally advanced rectal cancer (LARC)
neoadjuvant chemoradiotherapy (nCRT)
nomogram
pathological complete response (pCR)
radiomics signature
Journal
Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829
Informations de publication
Date de publication:
25 Oct 2023
25 Oct 2023
Historique:
received:
18
09
2023
revised:
15
10
2023
accepted:
19
10
2023
medline:
14
11
2023
pubmed:
14
11
2023
entrez:
14
11
2023
Statut:
epublish
Résumé
The objective of this study was to evaluate the discriminative capabilities of radiomics signatures derived from three distinct machine learning algorithms and to identify a robust radiomics signature capable of predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy in patients diagnosed with locally advanced rectal cancer (LARC). In a retrospective study, 211 LARC patients were consecutively enrolled and divided into a training cohort (
Identifiants
pubmed: 37958309
pii: cancers15215134
doi: 10.3390/cancers15215134
pmc: PMC10648149
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : National Natural Science Foundation of China
ID : 82073142
Organisme : National Natural Science Foundation of China
ID : 82103498
Organisme : National Natural Science Foundation of China
ID : 62103366
Organisme : Beijing Xisike Clinical Oncology Research Foundation
ID : Y-zai2022/qn-0213
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