Lung cancer is the most frequent cause of cancer-related death around the world. With the recent introduction of low-dose lung computed tomography for lung cancer screening, there has been an increasing number of smoking- and non-smoking-related lung cancer cases worldwide that are manifesting with subsolid nodules, especially in Asian populations. However, the pros and cons of lung cancer screening also follow the implementation of lung cancer screening programs. Here, we review the literature related to radiomics for early lung cancer diagnosis. There are four main radiomics applications: the classification of lung nodules as being malignant/benign; determining the degree of invasiveness of the lung adenocarcinoma; histopathologic subtyping; and prognostication in lung cancer prediction models. In conclusion, radiomics offers great potential to improve diagnosis and personalized risk stratification in early lung cancer diagnosis through patient–doctor cooperation and shared decision making.

Lung cancer has been the leading cause of cancer-related mortality worldwide among both men and women in recent years. There is an increase in the incidence of nonsmoking-related lung cancer in recent years. The purpose of the present study was to investigate multiple potential risk factors for nonsmoking-related lung cancer among Asian Ethnic Groups. We used a propensity score-mated cohort analysis for this study. We retrospectively review the medical record of 1975 asymptomatic healthy subjects (40 ~ 80 years old) who voluntarily underwent low-dose chest CT from August 2013 to October 2014. Clinical information and nodule characteristics were recorded. A propensity score-mated cohort analysis was applied to adjust for potential bias and to create two comparable groups according to family history of lung cancer. For our primary analysis, we matched 392 pairs of subjects with family history of lung cancer and subjects without history. Logistic regression showed that female gender and a family history of lung cancer were the two most important predictor of lung cancer in the endemic area with high prevalence of nonsmoking-related lung cancer (OR = 11.199, 95% CI = 1.444–86.862; OR = 2.831, 95% CI = 1.000136–8.015). In addition, the number of nodules was higher in subjects with family history of lung cancer in comparison with subjects without family history of lung cancer (OR = 1.309, 95% CI = 1.066–1.607). In conclusion, risk-based prediction model based on the family history of lung cancer and female gender can potentially improve efficiency of lung cancer screening programs in Taiwan.

OBJECTIVES: Some metabolic factors and noninvasive markers, including fatty liver index (FLI), are used to predict nonalcoholic fatty liver disease (NAFLD) in obese patients. Despite the increasing prevalence of NAFLD in lean patients (lean-NAFLD), the risk factors and predictors are not well determined in this population. We investigated factors associated with lean-NAFLD and validated their predictive ability. METHODS: From 9,293 examinees who underwent routine health checkups, we enrolled 4,000, aged ≥20 years, with a body mass index <24 kg/m² in our lean-NAFLD study population. NAFLD diagnoses were made according to the patients' histories, laboratory values, and sonographic criteria. Clinical variables, serum sugar, lipid, and liver profiles were evaluated using multiple logistic regression analysis. The predictive ability and optimal cutoff values for NAFLD were determined according to the area under the receiver operating characteristic curve. RESULTS: Overall, 18.5% (n = 740) of the lean population had NAFLD. Male sex, body mass index, body fat mass, fasting plasma glucose, uric acid, alanine aminotransferase, triglyceride, and FLI values were associated with NAFLD. FLI had the best discriminative ability to predict lean-NAFLD compared to the other biochemical markers. We further used the Youden index test and found an optimum cut-off value for FLI of 15 with the highest discriminant ability than other values. DISCUSSION: The prevalence of lean-NAFLD was not low. FLI was superior to other predictors including sex, liver function, and other metabolic factors, in the prediction of lean-NAFLD. FLI may be considered an easy to use, noninvasive marker to screen for lean-NAFLD.