Evaluating the Potential of Delta Radiomics for Assessing Tyrosine Kinase Inhibitor Treatment Response in Non-Small Cell Lung Cancer Patients

Ting Wei Wang; Heng Sheng Chao; Hwa Yen Chiu; Yi Hui Lin; Hung Chun Chen; Chia Feng Lu; Chien Yi Liao; Yen Lee; Tsu Hui Shiao; Yuh Min Chen; Jing Wen Huang; Yu Te Wu

doi:10.3390/cancers15215125

Evaluating the Potential of Delta Radiomics for Assessing Tyrosine Kinase Inhibitor Treatment Response in Non-Small Cell Lung Cancer Patients

Ting Wei Wang, Heng Sheng Chao, Hwa Yen Chiu, Yi Hui Lin, Hung Chun Chen, Chia Feng Lu, Chien Yi Liao, Yen Lee, Tsu Hui Shiao, Yuh Min Chen, Jing Wen Huang^*, Yu Te Wu^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

Our study aimed to harness the power of CT scans, observed over time, in predicting how lung adenocarcinoma patients might respond to a treatment known as EGFR-TKI. Analyzing scans from 322 advanced stage lung cancer patients, we identified distinct image-based patterns. By integrating these patterns with comprehensive clinical information, such as gene mutations and treatment regimens, our predictive capabilities were significantly enhanced. Interestingly, the precision of these predictions, particularly related to radiomics features, diminished when data from various centers were combined, suggesting that the approach requires standardization across facilities. This novel method offers a potential pathway to anticipate disease progression in lung adenocarcinoma patients treated with EGFR-TKI, laying the groundwork for more personalized treatments. To further validate this approach, extensive studies involving a larger cohort are pivotal.

Original language	English
Article number	5125
Journal	Cancers
Volume	15
Issue number	21
DOIs	https://doi.org/10.3390/cancers15215125
State	Published - Nov 2023

Keywords

computer tomography (CT) scans
delta radiomics signatures
epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKI)
lung adenocarcinoma
personalized treatment strategies
progression-free survival (PFS)
time-variable radiomics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/cancers15215125

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Wang, T. W., Chao, H. S., Chiu, H. Y., Lin, Y. H., Chen, H. C., Lu, C. F., Liao, C. Y., Lee, Y., Shiao, T. H., Chen, Y. M., Huang, J. W., & Wu, Y. T. (2023). Evaluating the Potential of Delta Radiomics for Assessing Tyrosine Kinase Inhibitor Treatment Response in Non-Small Cell Lung Cancer Patients. Cancers, 15(21), Article 5125. https://doi.org/10.3390/cancers15215125

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title = "Evaluating the Potential of Delta Radiomics for Assessing Tyrosine Kinase Inhibitor Treatment Response in Non-Small Cell Lung Cancer Patients",

abstract = "Our study aimed to harness the power of CT scans, observed over time, in predicting how lung adenocarcinoma patients might respond to a treatment known as EGFR-TKI. Analyzing scans from 322 advanced stage lung cancer patients, we identified distinct image-based patterns. By integrating these patterns with comprehensive clinical information, such as gene mutations and treatment regimens, our predictive capabilities were significantly enhanced. Interestingly, the precision of these predictions, particularly related to radiomics features, diminished when data from various centers were combined, suggesting that the approach requires standardization across facilities. This novel method offers a potential pathway to anticipate disease progression in lung adenocarcinoma patients treated with EGFR-TKI, laying the groundwork for more personalized treatments. To further validate this approach, extensive studies involving a larger cohort are pivotal.",

keywords = "computer tomography (CT) scans, delta radiomics signatures, epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKI), lung adenocarcinoma, personalized treatment strategies, progression-free survival (PFS), time-variable radiomics",

author = "Wang, {Ting Wei} and Chao, {Heng Sheng} and Chiu, {Hwa Yen} and Lin, {Yi Hui} and Chen, {Hung Chun} and Lu, {Chia Feng} and Liao, {Chien Yi} and Yen Lee and Shiao, {Tsu Hui} and Chen, {Yuh Min} and Huang, {Jing Wen} and Wu, {Yu Te}",

note = "Publisher Copyright: {\textcopyright} 2023 by the authors.",

year = "2023",

month = nov,

doi = "10.3390/cancers15215125",

language = "English",

volume = "15",

journal = "Cancers",

issn = "2072-6694",

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AU - Wang, Ting Wei

AU - Chao, Heng Sheng

AU - Chiu, Hwa Yen

AU - Lin, Yi Hui

AU - Chen, Hung Chun

AU - Lu, Chia Feng

AU - Liao, Chien Yi

AU - Lee, Yen

AU - Shiao, Tsu Hui

AU - Chen, Yuh Min

AU - Huang, Jing Wen

AU - Wu, Yu Te

PY - 2023/11

Y1 - 2023/11

N2 - Our study aimed to harness the power of CT scans, observed over time, in predicting how lung adenocarcinoma patients might respond to a treatment known as EGFR-TKI. Analyzing scans from 322 advanced stage lung cancer patients, we identified distinct image-based patterns. By integrating these patterns with comprehensive clinical information, such as gene mutations and treatment regimens, our predictive capabilities were significantly enhanced. Interestingly, the precision of these predictions, particularly related to radiomics features, diminished when data from various centers were combined, suggesting that the approach requires standardization across facilities. This novel method offers a potential pathway to anticipate disease progression in lung adenocarcinoma patients treated with EGFR-TKI, laying the groundwork for more personalized treatments. To further validate this approach, extensive studies involving a larger cohort are pivotal.

AB - Our study aimed to harness the power of CT scans, observed over time, in predicting how lung adenocarcinoma patients might respond to a treatment known as EGFR-TKI. Analyzing scans from 322 advanced stage lung cancer patients, we identified distinct image-based patterns. By integrating these patterns with comprehensive clinical information, such as gene mutations and treatment regimens, our predictive capabilities were significantly enhanced. Interestingly, the precision of these predictions, particularly related to radiomics features, diminished when data from various centers were combined, suggesting that the approach requires standardization across facilities. This novel method offers a potential pathway to anticipate disease progression in lung adenocarcinoma patients treated with EGFR-TKI, laying the groundwork for more personalized treatments. To further validate this approach, extensive studies involving a larger cohort are pivotal.

KW - computer tomography (CT) scans

KW - delta radiomics signatures

KW - epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKI)

KW - lung adenocarcinoma

KW - personalized treatment strategies

KW - progression-free survival (PFS)

KW - time-variable radiomics

UR - http://www.scopus.com/inward/record.url?scp=85176506056&partnerID=8YFLogxK

U2 - 10.3390/cancers15215125

DO - 10.3390/cancers15215125

M3 - Article

AN - SCOPUS:85176506056

SN - 2072-6694

VL - 15

JO - Cancers

JF - Cancers

IS - 21

M1 - 5125

ER -

Evaluating the Potential of Delta Radiomics for Assessing Tyrosine Kinase Inhibitor Treatment Response in Non-Small Cell Lung Cancer Patients

Abstract

Keywords

UN SDGs

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