Objective To predict the recurrence of non-small cell lung cancer (NSCLC) within 2 years after curative-intent treatment using a machine-learning approach with PET/ CT-based radiomics.Patients and methods A total of 77 NSCLC patients who underwent pretreatment 18 F-fluorodeoxyglucose PET/ CT were retrospectively analyzed. Five clinical features (age, sex, tumor stage, tumor histology, and smoking status) and 48 radiomic features extracted from primary tumors on PET were used for binary classifications. These were ranked, and a subset of useful features was selected based on Gini coefficient scores in terms of associations with relapsed status. Areas under the receiver operating characteristics curves (AUC) were yielded by six machinelearning algorithms (support vector machine, random forest, neural network, naive Bayes, logistic regression, and gradient boosting). Model performances were compared and validated via random sampling. ResultsA PET/CT-based radiomic model was developed and validated for predicting the recurrence of NSCLC during the first 2 years after curation. The most important features were SD and variance of standardized uptake value, followed by low-intensity short-zone emphasis and high-intensity zone emphasis. The naive Bayes model with the 15 best-ranked features displayed the best performance (AUC: 0.816). Prediction models using the five best PET-derived features outperformed those using five clinical variables. ConclusionThe machine learning model using PETderived radiomic features showed good performance for predicting the recurrence of NSCLC during the first 2 years after a curative intent therapy. PET/CT-based radiomic features may help clinicians improve the risk stratification of relapsed NSCLC.
Microdeletion of 9q22.3 is a rare chromosomal disorder characterized by body overgrowth, facial dysmorphic features and psychomotor delay. The presence of genomic microdeletion or microdu-plication can not be identified by the conventional chromosomal analysis. Microarray comparative genomic hybri dization (CGH) is a newly developed molecular cytogenetic technique that enables the identification of minute copy number variation (CNV) in the human genome. Here, we report a case of microdeletion in the 9q22.31-q22.33 region, which in cluded a patched homolog 1 (PTCH1) gene, as detected by CGH and confirmed by fluore scence in situ hybridization (FISH) analyses in a neonate with prenatal onset of macrosomia, dysmorphism, and muscle hypotonia. To the best of our knowledge, this is the first case report of 9q22.3 microdeletion detected by CGH in Korea.
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