TY - JOUR
T1 - Predicting lung cancer's metastats' locations using bioclinical model
AU - Lazebnik, Teddy
AU - Bunimovich-Mendrazitsky, Svetlana
N1 - Publisher Copyright:
Copyright © 2024 Lazebnik and Bunimovich-Mendrazitsky.
PY - 2024
Y1 - 2024
N2 - Background: Lung cancer is a global leading cause of cancer-related deaths, and metastasis profoundly influences treatment outcomes. The limitations of conventional imaging in detecting small metastases highlight the crucial need for advanced diagnostic approaches. Methods: This study developed a bioclinical model using three-dimensional CT scans to predict the spatial spread of lung cancer metastasis. Utilizing a three-layer biological model, we identified regions with a high probability of metastasis colonization and validated the model on real-world data from 10 patients. Findings: The validated bioclinical model demonstrated a promising 74% accuracy in predicting metastasis locations, showcasing the potential of integrating biophysical and machine learning models. These findings underscore the significance of a more comprehensive approach to lung cancer diagnosis and treatment. Interpretation: This study's integration of biophysical and machine learning models contributes to advancing lung cancer diagnosis and treatment, providing nuanced insights for informed decision-making.
AB - Background: Lung cancer is a global leading cause of cancer-related deaths, and metastasis profoundly influences treatment outcomes. The limitations of conventional imaging in detecting small metastases highlight the crucial need for advanced diagnostic approaches. Methods: This study developed a bioclinical model using three-dimensional CT scans to predict the spatial spread of lung cancer metastasis. Utilizing a three-layer biological model, we identified regions with a high probability of metastasis colonization and validated the model on real-world data from 10 patients. Findings: The validated bioclinical model demonstrated a promising 74% accuracy in predicting metastasis locations, showcasing the potential of integrating biophysical and machine learning models. These findings underscore the significance of a more comprehensive approach to lung cancer diagnosis and treatment. Interpretation: This study's integration of biophysical and machine learning models contributes to advancing lung cancer diagnosis and treatment, providing nuanced insights for informed decision-making.
KW - biophysical model
KW - clinical computer vision
KW - diagnosis support model
KW - metastasis detection
KW - spatial biology
UR - http://www.scopus.com/inward/record.url?scp=85195424400&partnerID=8YFLogxK
U2 - 10.3389/fmed.2024.1388702
DO - 10.3389/fmed.2024.1388702
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AN - SCOPUS:85195424400
SN - 2296-858X
VL - 11
JO - Frontiers in Medicine
JF - Frontiers in Medicine
M1 - 1388702
ER -