Published Paper Details:

Clinical practice faces a significant diagnostic challenge with pulmonary lesions, spanning a wide spectrum of anomalies within the lung parenchyma, from benign nodules to malignant tumors. While traditional imaging methods like computed tomography (CT) are crucial for lesion detection, relying solely on qualitative assessment often leads to imprecise lesion definition. In contrast, quantitative CT analysis offers a sophisticated approach to obtaining accurate quantitative metrics from radiological images, enabling objective and consistent evaluation of lesion morphology, density, and texture. This study aims to explore the potential revolutionary benefits of quantitative CT analysis in characterizing pulmonary lesions, shedding light on their biology, prognosis, and treatment responsiveness. Through a comprehensive review of existing research and empirical data analysis, we elucidate the clinical implications and translational prospects of quantitative CT analysis in pulmonary medicine. Furthermore, we discuss the emerging role of radiomics, employing advanced machine learning techniques, including Attention U-Net, to unravel the molecular heterogeneity underlying pulmonary diseases. The integration of quantitative CT analysis into the diagnostic toolkit of pulmonary medicine heralds a new era of more accurate and individualized patient care. By leveraging quantitative measures beyond the constraints of qualitative assessment, we seek to revolutionize pulmonary lesion evaluation and push the boundaries of clinical practice, ultimately leading to improved patient outcomes.