Radiomics predicts response of individual HER2-amplified colorectal cancer liver metastases in patients treated with HER2-targeted therapy.
Aged
Algorithms
Colorectal Neoplasms
/ diagnostic imaging
Female
Humans
Liver Neoplasms
/ diagnostic imaging
Machine Learning
Male
Middle Aged
Molecular Targeted Therapy
Protein Kinase Inhibitors
/ therapeutic use
Receptor, ErbB-2
/ genetics
Sensitivity and Specificity
Survival Analysis
Tomography, X-Ray Computed
/ methods
Treatment Outcome
CT liver metastases
genetic algorithms
machine learning
prediction of response to therapy
radiomics
Journal
International journal of cancer
ISSN: 1097-0215
Titre abrégé: Int J Cancer
Pays: United States
ID NLM: 0042124
Informations de publication
Date de publication:
01 12 2020
01 12 2020
Historique:
received:
27
04
2020
revised:
06
08
2020
accepted:
12
08
2020
pubmed:
3
9
2020
medline:
17
4
2021
entrez:
3
9
2020
Statut:
ppublish
Résumé
The aim of our study was to develop and validate a machine learning algorithm to predict response of individual HER2-amplified colorectal cancer liver metastases (lmCRC) undergoing dual HER2-targeted therapy. Twenty-four radiomics features were extracted after 3D manual segmentation of 141 lmCRC on pretreatment portal CT scans of a cohort including 38 HER2-amplified patients; feature selection was then performed using genetic algorithms. lmCRC were classified as nonresponders (R-), if their largest diameter increased more than 10% at a CT scan performed after 3 months of treatment, responders (R+) otherwise. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values in correctly classifying individual lesion and overall patient response were assessed on a training dataset and then validated on a second dataset using a Gaussian naïve Bayesian classifier. Per-lesion sensitivity, specificity, NPV and PPV were 89%, 85%, 93%, 78% and 90%, 42%, 73%, 71% respectively in the testing and validation datasets. Per-patient sensitivity and specificity were 92% and 86%. Heterogeneous response was observed in 9 of 38 patients (24%). Five of nine patients were carriers of nonresponder lesions correctly classified as such by our radiomics signature, including four of seven harboring only one nonresponder lesion. The developed method has been proven effective in predicting behavior of individual metastases to targeted treatment in a cohort of HER2 amplified patients. The model accurately detects responder lesions and identifies nonresponder lesions in patients with heterogeneous response, potentially paving the way to multimodal treatment in selected patients. Further validation will be needed to confirm our findings.
Substances chimiques
Protein Kinase Inhibitors
0
ERBB2 protein, human
EC 2.7.10.1
Receptor, ErbB-2
EC 2.7.10.1
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3215-3223Informations de copyright
© 2020 Union for International Cancer Control.
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