Research led by Georgi Nalbantov PhD, Head of Data Science @ VectorLabs.AI, to examine the impact of pre-existing cardiac conditions on radiation-induced lung toxicity (RILT) after high-dose radio(chemo)therapy for lung cancer. 
 
Overview:

<ul>
	<li>Traditionally, radiation effect on the heart and lungs were studied separately</li>
	<li>Recent studies reveal a short-term interaction between heart irradiation and lung dysfunction</li>
	<li>This study bridges the gap by investigating how cardiac issues affect RILT in the short term&nbsp;</li>
</ul>

Observations:

<ul>
	<li>28.9% of patients had cardiac comorbidity before radiotherapy</li>
	<li>44% of patients had cardiac comorbidity before radiotherapy</li>
	<li>Patients with cardiac comorbidity had 2.58 times higher odds of developing RILT</li>
</ul>

Predictive model for RILT:

<ul>
	<li>A model was built which helps identify patients at higher risk of RILT</li>
	<li>This model incorporates cardiac comorbidity, tumour location, lung function, chemotherapy, and pretreatment dyspnea score</li>
	<li>The model demonstrated an Area Under the Curve (AUC) of 0.72 in the training set and 0.67 in the validation set, signifying its predictive performance</li>
</ul>

&nbsp;

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Key implications:

<ul>
	<li>Cardiac comorbidity contributes significantly to RILT cases</li>
	<li>Tailored treatment approaches are essential for patients with cardiac comorbidity</li>
	<li>Excluding these patients from dose escalation studies may optimize outcomes</li>
</ul>

Read more in the<a href="https://www.sciencedirect.com/science/article/pii/S0167814013004350" target="_blank"> full paper here</a>.&nbsp;

Georgi Nalbantov, PhD, Head of Data Science at VectorLabs.AI explores in a scientific study the impact of cardiac comorbidity on Radiation-Induced Lung Toxicity (RILT) in lung cancer patients.

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