Augmentation of Radioiodine Uptake by Pulmonary Metastasis of Papillary Thyroid Carcinoma Treated with Dabrafenib and Trametinib.

Redifferentiation therapy with Dabrafenib (a BRAF inhibitor) and Trametinib (a MEK inhibitor) restores radioiodine avidity of radioiodine-refractory papillary thyroid carcinoma (PTC). A 50-year-old man was diagnosed with radioiodine-refractory PTC pulmonary metastasis post prior total thyroidectomy and radioiodine ablation. The patient was treated with Dabrafenib and Trametinib, followed by second radioiodine ablation with I-131 sodium iodine. Diffuse increased radioiodine uptake by pulmonary metastasis was visualized on post ablation whole body scan. Response to second radioiodine ablation was demonstrated by decrease in size of pulmonary nodules seen on chest CT, along with decrease of thyroglobulin level.

Deep Learning for Detection of Pneumothorax and Pleural Effusion on Chest Radiographs: Validation Against Computed Tomography, Impact on Resident Reading Time, and Interreader Concordance.

PURPOSE: To study the performance of artificial intelligence (AI) for detecting pleural pathology on chest radiographs (CXRs) using computed tomography as ground truth. PATIENTS AND METHODS: Retrospective study of subjects undergoing CXR in various clinical settings. Computed tomography obtained within 24 hours of the CXR was used to volumetrically quantify pleural effusions (PEfs) and pneumothoraxes (Ptxs). CXR was evaluated by AI software (INSIGHT CXR; Lunit) and by 3 second-year radiology residents, followed by AI-assisted reassessment after a 3-month washout period. We used the area under the receiver operating characteristics curve (AUROC) to assess AI versus residents' performance and mixed-model analyses to investigate differences in reading time and interreader concordance. RESULTS: There were 96 control subjects, 165 with PEf, and 101 with Ptx. AI-AUROC was noninferior to aggregate resident-AUROC for PEf (0.82 vs 0.86, P < 0.001) and Ptx (0.80 vs 0.84, P = 0.001) detection. AI-assisted resident-AUROC was higher but not significantly different from the baseline. AI-assisted reading time was reduced by 49% (157 vs 80 s per case, P = 0.009), and Fleiss kappa for Ptx detection increased from 0.70 to 0.78 ( P = 0.003). AI decreased detection error for PEf (odds ratio = 0.74, P = 0.024) and Ptx (odds ratio = 0.39, P < 0.001). CONCLUSION: Current AI technology for the detection of PEf and Ptx on CXR was noninferior to second-year resident performance and could help decrease reading time and detection error.