ABSTRACT
BACKGROUND: Owing to the cytotoxic effect, it is challenging for clinicians to decide whether post-operative adjuvant therapy is appropriate for a non-small cell lung cancer (NSCLC) patient. Radiomics has proven its promising ability in predicting survival but research on its actionable model, particularly for supporting the decision of adjuvant therapy, is limited. METHODS: Pre-operative contrast-enhanced CT images of 123 NSCLC cases were collected, including 76, 13, 16, and 18 cases from R01 and AMC cohorts of The Cancer Imaging Archive (TCIA), Jiangxi Cancer Hospital and Guangdong Provincial People's Hospital respectively. From each tumor region, 851 radiomic features were extracted and two augmented features were derived therewith to estimate the likelihood of adjuvant therapy. Both Cox regression and machine learning models with the selected main and interaction effects of 853 features were trained using 76 cases from R01 cohort, and their test performances on survival prediction were compared using 47 cases from the AMC cohort and two hospitals. For those cases where adjuvant therapy was unnecessary, recommendations on adjuvant therapy were made again by the outperforming model and compared with those by IBM Watson for Oncology (WFO). RESULTS: The Cox model outperformed the machine learning model in predicting survival on the test set (C-Index: 0.765 vs. 0.675). The Cox model consists of 5 predictors, interestingly 4 of which are interactions with augmented features facilitating the modulation of adjuvant therapy option. While WFO recommended no adjuvant therapy for only 13.6% of cases that received unnecessary adjuvant therapy, the same recommendations by the identified Cox model were extended to 54.5% of cases (McNemar's test p = 0.0003). CONCLUSIONS: A Cox model with radiomic and augmented features could predict survival accurately and support the decision of adjuvant therapy for bettering the benefit of NSCLC patients.
ABSTRACT
OBJECTIVE: To compare the applied value of the pressure aggravation test and breath aggravation test in the diagnosis of early acute appendicitis. METHODS: A total of 101 cases with epigastralgia, middle or upper abdomen pain, disease duration within 6 hours undergoing pressure aggravation test and breath aggravation test respectively in our hospital between October 2010 and December 2012 were prospectively enrolled. By comparing with the postoperative pathological diagnosis (early acute appendicitis and other abdominal pain), the sensitivity and specificity of these two tests were calculated. Through analyzing the receiver operating characteristic (ROC) curve, the diagnostic value of early acute appendicitis was evaluated. RESULTS: Fifty-two cases of early acute appendicitis and 49 cases of other abdominal pain were diagnosed by postoperative pathologic results. The sensitivity and specificity of the pressure aggravation test were 87.5% and 72.1% and of the breath aggravation test were 53.8% and 83.7% respectively. The area under the ROC curve of the pressure aggravation test was 0.786 (95% CI: 0.693-0.878), similar to that of the breath aggravation test (0.688, 95% CI: 0.583-0.792). CONCLUSION: The pressure aggravation test has higher value to diagnose early acute appendicitis, while the breath aggravation test has better specificity.
