Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases
Objectives: This study explored the clinical utility of CT radiomics-driven machine learning as a predictive marker for chemotherapy response in colorectal liver metastasis (CRLM) patients. Methods: We included 150 CRLM patients who underwent first-line doublet chemotherapy, dividing them into a tra...
Saved in:
Main Authors: | Yuji Miyamoto, Takeshi Nakaura, Mayuko Ohuchi, Katsuhiro Ogawa, Rikako Kato, Yuto Maeda, Kojiro Eto, Masaaki Iwatsuki, Yoshifumi Baba, Toshinori Hirai, Hideo Baba |
---|---|
Format: | Article |
Language: | English |
Published: |
The Japan Society of Coloproctology
2025-01-01
|
Series: | Journal of the Anus, Rectum and Colon |
Subjects: | |
Online Access: | https://www.jstage.jst.go.jp/article/jarc/9/1/9_2024-077/_pdf/-char/en |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Lung Metastases to the Heart with Atypical Clinical Manifestations of Cardiac Failure
by: Natalia HYRIAVENKO, et al.
Published: (2023-03-01) -
Omental metastases in patients with pseudomyxoma peritonei or colorectal peritoneal metastases – is routine omentectomy justified?
by: Malin Enblad, et al.
Published: (2024-12-01) -
Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features
by: Hemalatha Kanakarajan, et al.
Published: (2024-12-01) -
Utilizing machine-learning techniques on MRI radiomics to identify primary tumors in brain metastases
by: W. L. Yang, et al.
Published: (2025-01-01) -
Clinical Characteristics of Choroidal Metastases
by: Bożena Romanowska-Dixon, et al.
Published: (2024-10-01)