Identifying LDCT imaging features associated with second primary lung cancer risk among lung cancer patients
Principal Investigator
Name
Summer Han
Degrees
Ph.D
Institution
Stanford University School of Medicine
Position Title
Associate Professor
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-1171
Initial CDAS Request Approval
Dec 18, 2023
Title
Identifying LDCT imaging features associated with second primary lung cancer risk among lung cancer patients
Summary
Recent advances in early detection and treatment have doubled the 5-year survival rates among lung cancer patients. As a result, the number of lung cancer survivors is expected to grow rapidly. However, the extended survivals also give rise to another risk of developing second primary lung cancer (SPLC). In fact, recent studies have shown that lung cancer patients have a 4-6 times higher risk of developing SPLC compared to the risk of developing initial primary lung cancer (IPLC) in the general population. More importantly, the IPLC patients who further develop SPLC have significantly worse survival than those who remain with single primary lung cancer.
Over the past decades, low-dose computed tomography (LDCT) has transformed the field of lung cancer screening and surveillance. Along with the popularity of LDCT screening, an increasing number of lung ground-glass opacity (GGO) lesions are detected. Although GGO, different from a solid nodule or carcinoma in situ, is a non-specific radiologic finding, it has the potential to evolve into a malignant lesion, i.e., SPLC. However, what characteristics of specific GGO among IPLC patients are associated with the one that eventually develop and grow as SPLC is unknown. Given that LDCT imaging features are highly predictive of malignancy of a GGO and subsolid nodule in the general population (Baldwin DR, et al. [Thorax. 2020]; Massion PP, et al. [AJRCM. 2020]), Mikhael et al. (JCO, 2023) developed and validated a LDCT image-based deep learning model (called Sybil) to predict IPLC risk in the general population. So, we would like to examine whether the performance of the LDCT imaging-based deep learning tool (Sybil) can identify lung cancer survivors who have a high risk of SPLC. As the next step, we aim to identify the LDCT imaging features of pulmonary nodules associated with SPLC risk among lung cancer survivors, and to evaluate the predictive potential of the imaging features using radiomics and deep learning methods.
Over the past decades, low-dose computed tomography (LDCT) has transformed the field of lung cancer screening and surveillance. Along with the popularity of LDCT screening, an increasing number of lung ground-glass opacity (GGO) lesions are detected. Although GGO, different from a solid nodule or carcinoma in situ, is a non-specific radiologic finding, it has the potential to evolve into a malignant lesion, i.e., SPLC. However, what characteristics of specific GGO among IPLC patients are associated with the one that eventually develop and grow as SPLC is unknown. Given that LDCT imaging features are highly predictive of malignancy of a GGO and subsolid nodule in the general population (Baldwin DR, et al. [Thorax. 2020]; Massion PP, et al. [AJRCM. 2020]), Mikhael et al. (JCO, 2023) developed and validated a LDCT image-based deep learning model (called Sybil) to predict IPLC risk in the general population. So, we would like to examine whether the performance of the LDCT imaging-based deep learning tool (Sybil) can identify lung cancer survivors who have a high risk of SPLC. As the next step, we aim to identify the LDCT imaging features of pulmonary nodules associated with SPLC risk among lung cancer survivors, and to evaluate the predictive potential of the imaging features using radiomics and deep learning methods.
Aims
(1) To evaluate the predictive accuracy of SYBIL lung cancer risk prediction model for SPLC among LC survivors using NLST data
(2) To identify the LDCT imaging features of pulmonary nodules (especially focusing on GGOs and subsolid nodules) associated with SPLC risk among lung cancer survivors
(3) To evaluate the predictive potential of the imaging features using radiomics and deep learning methods
Collaborators
Summer Han; Eunji Choi