Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis.

OBJECTIVES: To investigate the usefulness of machine learning (ML) models using pretreatment 18F-FDG-PET-based radiomic features for predicting adverse clinical events (ACEs) in patients with cardiac sarcoidosis (CS). MATERIALS AND METHODS: This retrospective study included 47 patients with CS who underwent 18F-FDG-PET/CT scan before treatment. The lesions were assigned to the training (n = 38) and testing (n = 9) cohorts. In total, 49 18F-FDG-PET-based radiomic features and the visibility of right ventricle 18F-FDG uptake were used to predict ACEs using seven different ML algorithms (namely, decision tree, random forest [RF], neural network, k-nearest neighbors, Naïve Bayes, logistic regression, and support vector machine [SVM]) with tenfold cross-validation and the synthetic minority over-sampling technique. The ML models were constructed using the top four features ranked by the decrease in Gini impurity. The AUCs and accuracies were used to compare predictive performances. RESULTS: Patients who developed ACEs presented with a significantly higher surface area and gray level run length matrix run length non-uniformity (GLRLM_RLNU), and lower neighborhood gray-tone difference matrix_coarseness and sphericity than those without ACEs (each, p < 0.05). In the training cohort, all seven ML algorithms had a good classification performance with AUC values of > 0.80 (range: 0.841-0.944). In the testing cohort, the RF algorithm had the highest AUC and accuracy (88.9% [8/9]) with a similar classification performance between training and testing cohorts (AUC: 0.945 vs 0.889). GLRLM_RLNU was the most important feature of the modeling process of this RF algorithm. CONCLUSION: ML analyses using 18F-FDG-PET-based radiomic features may be useful for predicting ACEs in patients with CS.

Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology.

Machine learning (ML) analyses using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomics features have been applied in the field of oncology. The current review aimed to summarize the current clinical articles about 18F-FDG PET/CT radiomics-based ML analyses to solve issues in classifying or constructing prediction models for several types of tumors. In these studies, lung and mediastinal tumors were the most commonly evaluated lesions, followed by lymphatic, abdominal, head and neck, breast, gynecological, and other types of tumors. Previous studies have commonly shown that 18F-FDG PET radiomics-based ML analysis has good performance in differentiating benign from malignant tumors, predicting tumor characteristics and stage, therapeutic response, and prognosis by examining significant differences in the area under the receiver operating characteristic curves, accuracies, or concordance indices (> 0.70). However, these studies have reported several ML algorithms. Moreover, different ML models have been applied for the same purpose. Thus, various procedures were used in 18F-FDG PET/CT radiomics-based ML analysis in oncology, and 18F-FDG PET/CT radiomics-based ML models, which are easy and universally applied in clinical practice, would be expected to be established.

CT and MRI features of undifferentiated carcinomas with osteoclast-like giant cells of the pancreas: a case series.

PURPOSE: The purpose of this case series was to describe computed tomography (CT) and magnetic resonance imaging (MRI) features of undifferentiated carcinoma with osteoclast-like giant cells of the pancreas. METHODS: Institutional ethics review board approval was obtained, and informed consent was waived for this case series. We reviewed CT and MRI of patients with pathologically confirmed undifferentiated carcinoma with osteoclast-like giant cells of the pancreas found in the medical records of our hospital between 2006 and 2016. RESULTS: Seven patients (3 males and 4 females; age, 59-82 years (mean, 71)) with confirmation by surgical resection (n = 3) or biopsy (n = 4) were identified. They underwent CT (n = 7) and MRI (n = 6). The tumors 26-83 mm in diameter (mean, 44 mm) were located in the head (n = 4) or body (n = 3) of the pancreas. They were demonstrated as lower attenuation areas relative to the adjacent pancreas on CT images obtained in both pancreatic and portal vein phases (n = 7) with a well-defined smooth margin (n = 5). They were demonstrated as hypointense areas relative to the pancreas on T2-weighted images (n = 4) and T2*-weighted images (n = 4) and diffusion-weighted images (n = 3). They contained hemosiderin deposits on histology (n = 7). CONCLUSIONS: Undifferentiated carcinoma with osteoclast-like giant cells of the pancreas might be present as low attenuation areas with a well-defined smooth margin on CT images obtained in pancreatic and portal vein phases, and hypointense areas on T2-, T2*-, and diffusion-weighted images caused by hemosiderin deposits.