No description available.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23782号 / 医博第4828号 / 新制||医||1057(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 羽賀 博典, 教授 波多野 悦朗, 教授 溝脇 尚志 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

No description available.

No description available.

No description available.

No description available.

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No description available.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23784号 / 医博第4830号 / 新制||医||1057(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 鈴木 実, 教授 中島 貴子, 教授 伊達 洋至 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Lung cancer currently has the greatest mortality rate of cancer patients of all sexes in the United States (Torre et al., 2016). Low-dose CT scans are utilized for lung cancer screening in patients who fall within the NLST entry criteria (Sanchez-Salcedo et al., 2015). The original criteria for screening were age over 55 and pack-year over 30, which were recently changed to age 50 and pack-year over 20 in 2021. The study population in this paper utilized the original criteria. A system developed and copyrighted by the American College of Radiology (ACR) referred to as the Lung CT Screening Reporting and Data System (Lung-RADS) has implemented a standardized method of classifying and interpreting lung cancer chest CT screening results. Lung-RADS has a scoring system which is scaled 1 – 4x (Pinsky et al., 2015) The likelihood of malignancy based on nodule appearance, diameter, and presence of growth comprise the components of which score is given (Chung et al., 2017). Lung-RADS 2 scored nodules are benign nodules and patients follow up for another CT in a year. Lung-RADS 3 nodules are probably benign nodules; however, they do have a low-risk of malignancy. It is known that a select few of these relatively benign appearing nodules will turn out to be malignant. Lung-RADS 4 nodules have a >5% chance of malignancy and can be confirmed through pathology. In this project, a retrospective chart review analyzing patient demographics and pulmonary health history will be correlated to lung-RADS risk likelihood of malignancy. Machine learning will also be utilized to study and analyze radiographic factors associated with the sample. The CT scans of patients who previously scored in an intermediate category will be compiled and analyzed to determine potential common demographical, clinical and radiomic factors which will hopefully allow intermediately categorized nodule indicators to be used to detect cancers earlier and to more accurately classify lesions into benign or malignant categories. In all, the goal of this research is to determine common clinical, demographic and radiomic factors of patients who were deemed intermediate risk and then progressed to a higher categorization. The importance of expanding current risk factors for discrimination of benign from malignant will also be analyzed, along with those specific risk-factors within Lung-RADS intermediately categorized nodules. The characteristics and baseline co-morbidities of RADS 2 and 3 lung cancer patients by follow-up CT results, progression to RADS-4 on follow-up CT and lung cancer diagnosis will be compiled and exemplified.