68Ga-DOTATATE Uptake in an Endolymphatic Sac Tumor: Radiologic-Pathologic Correlation.

After dedicated CT and MRI, Ga-DOTATATE PET/CT was performed in a patient with a temporal bone mass with primary diagnostic considerations of an endolymphatic sac tumor versus a glomus jugulotympanicum paraganglioma. The Ga-DOTATATE PET showed mild radiotracer uptake in the mass (SUVmax, 10.9). After surgical resection, pathology revealed an endolymphatic sac tumor. Immunohistochemical staining demonstrated somatostatin receptor type 2A expression in the vasculature of the mass, but not in the tumor cells.

Automated Detection of Radiology Reports that Require Follow-up Imaging Using Natural Language Processing Feature Engineering and Machine Learning Classification.

While radiologists regularly issue follow-up recommendations, our preliminary research has shown that anywhere from 35 to 50% of patients who receive follow-up recommendations for findings of possible cancer on abdominopelvic imaging do not return for follow-up. As such, they remain at risk for adverse outcomes related to missed or delayed cancer diagnosis. In this study, we develop an algorithm to automatically detect free text radiology reports that have a follow-up recommendation using natural language processing (NLP) techniques and machine learning models. The data set used in this study consists of 6000 free text reports from the author's institution. NLP techniques are used to engineer 1500 features, which include the most informative unigrams, bigrams, and trigrams in the training corpus after performing tokenization and Porter stemming. On this data set, we train naive Bayes, decision tree, and maximum entropy models. The decision tree model, with an F1 score of 0.458 and accuracy of 0.862, outperforms both the naive Bayes (F1 score of 0.381) and maximum entropy (F1 score of 0.387) models. The models were analyzed to determine predictive features, with term frequency of n-grams such as "renal neoplasm" and "evalu with enhanc" being most predictive of a follow-up recommendation. Key to maximizing performance was feature engineering that extracts predictive information and appropriate selection of machine learning algorithms based on the feature set.