Certain inflammatory pancreatic abnormalities may mimic pancreatic ductal adenocarcinoma at imaging, which precludes accurate preoperative diagnosis and may lead to unnecessary surgery. Inflammatory conditions that may appear masslike include massforming chronic pancreatitis, focal autoimmune pancreatitis, and paraduodenal pancreatitis or "groove pancreatitis." In addition, obstructive chronic pancreatitis can mimic an obstructing ampullary mass or main duct intraductal papillary mucinous neoplasm. Secondary imaging features such as the duct-penetrating sign, biliary or main pancreatic duct skip strictures, a capsulelike rim, the pancreatic duct-to-parenchyma ratio, displaced calcifications in patients with chronic calcific pancreatitis, the "double duct" sign, and vessel encasement or displacement can help to suggest the possibility of an inflammatory mass or a neoplastic process. An awareness of the secondary signs that favor a diagnosis of malignant or inflammatory lesions in the pancreas can help the radiologist to perform the differential diagnosis and determine the degree of suspicion for malignancy. Repeat biopsy or surgical resection may be necessary to achieve an accurate diagnosis and prevent unnecessary surgery for inflammatory conditions.

The large language model called ChatGPT has drawn extensively attention because of its human-like expression and reasoning abilities. In this study, we investigate the feasibility of using ChatGPT in experiments on translating radiology reports into plain language for patients and healthcare providers so that they are educated for improved healthcare. Radiology reports from 62 low-dose chest computed tomography lung cancer screening scans and 76 brain magnetic resonance imaging metastases screening scans were collected in the first half of February for this study. According to the evaluation by radiologists, ChatGPT can successfully translate radiology reports into plain language with an average score of 4.27 in the five-point system with 0.08 places of information missing and 0.07 places of misinformation. In terms of the suggestions provided by ChatGPT, they are generally relevant such as keeping following-up with doctors and closely monitoring any symptoms, and for about 37% of 138 cases in total ChatGPT offers specific suggestions based on findings in the report. ChatGPT also presents some randomness in its responses with occasionally over-simplified or neglected information, which can be mitigated using a more detailed prompt. Furthermore, ChatGPT results are compared with a newly released large model GPT-4, showing that GPT-4 can significantly improve the quality of translated reports. Our results show that it is feasible to utilize large language models in clinical education, and further efforts are needed to address limitations and maximize their potential.

In this report, we present the case of a 66-year-old man who received local consolidation radiotherapy to the right lung and mediastinum for oligometastatic non-small cell lung cancer (NSCLC) following partial response to upfront chemoimmunotherapy. He continued with maintenance immunotherapy and was asymptomatic for eight months after completing radiation therapy. He then developed symptoms consistent with pneumonitis within three to five days of his first administration of the coronavirus disease 2019 (COVID-19) vaccine injection. He reported that these symptoms significantly intensified within three to five days of receiving his second dose of the vaccine. The clinical time frame and radiographic evidence raised suspicion for radiation recall pneumonitis (RRP). Patients undergoing maintenance immunotherapy after prior irradiation may be at increased risk of this phenomenon that may be triggered by the administration of the COVID-19 vaccine.

We introduce a multi-institutional data harvesting (MIDH) method for longitudinal observation of medical imaging utilization and reporting. By tracking both large-scale utilization and clinical imaging results data, the MIDH approach is targeted at measuring surrogates for important disease-related observational quantities over time. To quantitatively investigate its clinical applicability, we performed a retrospective multi-institutional study encompassing 13 healthcare systems throughout the United States before and after the 2020 COVID-19 pandemic. Using repurposed software infrastructure of a commercial AI-based image analysis service, we harvested data on medical imaging service requests and radiology reports for 40,037 computed tomography pulmonary angiograms (CTPA) to evaluate for pulmonary embolism (PE). Specifically, we compared two 70-day observational periods, namely (i) a pre-pandemic control period from 11/25/2019 through 2/2/2020, and (ii) a period during the early COVID-19 pandemic from 3/8/2020 through 5/16/2020. Natural language processing (NLP) on final radiology reports served as the ground truth for identifying positive PE cases, where we found an NLP accuracy of 98% for classifying radiology reports as positive or negative for PE based on a manual review of 2,400 radiology reports. Fewer CTPA exams were performed during the early COVID-19 pandemic than during the pre-pandemic period (9806 vs. 12,106). However, the PE positivity rate was significantly higher (11.6 vs. 9.9%, p < 10−4) with an excess of 92 PE cases during the early COVID-19 outbreak, i.e., ~1.3 daily PE cases more than statistically expected. Our results suggest that MIDH can contribute value as an exploratory tool, aiming at a better understanding of pandemic-related effects on healthcare.