• The American Cancer Society 2035 challenge goal on cancer mortality reduction

      Ma, J.; Jemal, A.; Fedewa, S.A. (Wiley-Blackwell, 2019)
      A summary evaluation of the 2015 American Cancer Society (ACS) challenge goal showed that overall US mortality from all cancers combined declined 26% over the period from 1990 to 2015. Recent research suggests that US cancer mortality can still be lowered considerably by applying known interventions broadly and equitably. The ACS Board of Directors, therefore, commissioned ACS researchers to determine challenge goals for reductions in cancer mortality by 2035. A statistical model was used to estimate the average annual percent decline in overall cancer death rates among the US general population and among college-educated Americans during the most recent period. Then, the average annual percent decline in the overall cancer death rates of college graduates was applied to the death rates in the general population to project future rates in the United States beginning in 2020. If overall cancer death rates from 2020 through 2035 nationally decline at the pace of those of college graduates, then death rates in 2035 in the United States will drop by 38.3% from the 2015 level and by 54.4% from the 1990 level. On the basis of these results, the ACS 2035 challenge goal was set as a 40% reduction from the 2015 level. Achieving this goal could lead to approximately 1.3 million fewer cancer deaths than would have occurred from 2020 through 2035 and 122,500 fewer cancer deaths in 2035 alone. The results also show that reducing the prevalence of risk factors and achieving optimal adherence to evidence-based screening guidelines by 2025 could lead to a 33.5% reduction in the overall cancer death rate by 2035, attaining 85% of the challenge goal.
    • A prediction model based on biomarkers and clinical characteristics for detection of lung cancer in pulmonary nodules

      Ma, J.; Guarnera, M.A.; Zhou, W. (Translational Oncology Editorial Office, 2017)
      Lung cancer early detection by low-dose computed tomography (LDCT) can reduce the mortality. However, LDCT increases the number of indeterminate pulmonary nodules (PNs), whereas 95% of the PNs are ultimately false positives. Modalities for specifically distinguishing between malignant and benign PNs are urgently needed. We previously identified a panel of peripheral blood mononucleated cell (PBMC)-miRNA (miRs-19b-3p and -29b-3p) biomarkers for lung cancer. This study aimed to evaluate efficacy of integrating biomarkers and clinical and radiological characteristics of smokers for differentiating malignant from benign PNs. We analyzed expression of 2 miRNAs (miRs-19b-3p and -29b-3p) in PBMCs of a training set of 137 individuals with PNs. We used multivariate logistic regression analysis to develop a prediction model based on the biomarkers, radiographic features of PNs, and clinical characteristics of smokers for identifying malignant PNs. The performance of the prediction model was validated in a testing set of 111 subjects with PNs. A prediction model comprising the two biomarkers, spiculation of PNs and smoking pack-year, was developed that had 0.91 area under the curve of the receiver operating characteristic for distinguishing malignant from benign PNs. The prediction model yielded higher sensitivity (80.3% vs 72.6%) and specificity (89.4% vs 81.9%) compared with the biomarkers used alone (all P <.05). The performance of the prediction model for malignant PNs was confirmed in the validation set. We have for the first time demonstrated that the integration of biomarkers and clinical and radiological characteristics could efficiently identify lung cancer among indeterminate PNs. Copyright 2016 The Authors.