Synthetic radiographs, including OpenAI’s ChatGPT-generated ones, are not easily distinguishable from authentic radiographs by either radiologists or large language models; physician training and dedicated tools are crucial to mitigate risks.

Recently, there has been tremendous interest on the use of large language models (LLMs) in radiology. LLMs have been employed for various applications in cancer imaging, including improving reporting speed and accuracy via generation of standardized reports, automating the classification and staging of abnormal findings in reports, incorporating appropriate guidelines, and calculating individualized risk scores. Another use of LLMs is their ability to improve patient comprehension of imaging reports with simplification of the medical terms and possible translations to multiple languages. Additional future applications of LLMs include multidisciplinary tumor board standardizations, aiding patient management, and preventing and predicting adverse events (contrast allergies, MRI contraindications) and cancer imaging research. However, limitations such as hallucinations and variable performances could present obstacles to widespread clinical implementation. Herein, we present a review of the current and future applications of LLMs in cancer imaging, as well as pitfalls and limitations.

Background: The purpose of this study was to determine the long‐term prognostic implications of incidental pleural effusion (PE) detected during echocardiographic examination and its relationship with concomitant diseases.Hypothesis: The study hypothesis is to test whether incidental pleural PE detected during echocardiographic examination be used as a prognostic marker.Methods: The study was performed by evaluating patient records (N = 251) in whom PE was incidentally detected during echocardiographic examination in a tertiary hospital between 1999 and 2012. The patients were classified into 4 major groups according to the concomitant primary disease: malignancy, and cardiovascular, renal, and pulmonary diseases. The total survival time was obtained from hospital records for patients who died during the hospital stay and social security institution records for patients with out‐of‐hospital death.Results: One‐year and 5‐year life expectancies of PE cases concomitant with different disorders were as follows; heart failure (n = 151), 81% and 70%; malignancies (n = 45), 53% and 44%; pulmonary diseases (n = 37), 89% and 78%; renal diseases (n = 18), 100% and 83%; respectively. PE associated with heart failure, renal disease, and pulmonary disease had similar (P > 0.05 for all) and favorable outcomes compared to PE associated with malignancies (P < 0.001).Conclusions: The prognosis of incidental PE was the worst in patients with concomitant malignancies; however, PE associated with nonmalignant diseases including heart failure, pulmonary disease, and renal disease have similar and favorable outcomes.

Objective: Not only the frequency of surgery for small renal masses has increased secondary to the improvements and frequent use of imaging techniques but also the frequency of detection of benign lesions in nephrectomy specimens has increased as well. We aimed to assess the predictive value of computed tomography density of perirenal adipose tissue and peritumoral adipose tissue in distinguishing between benign and malignant renal masses. Materials and Methods: The current study included 116 patients who underwent nephrectomy for renal masses between January 2015 and December 2020. Clinicodemographic and preoperative computed tomography features and final pathological findings of the patients were recorded. According to the final pathological results, the patients were divided into 2 groups benign (n = 32) and malignant (n = 84). Groups were compared statistically in terms of perirenal adipose tissue and peritumoral adipose tissue density. Results: The median tumor size was 5.00 cm. The rate of benign tumors was higher in female patients (P = .005). The median peritumoral adipose tissue density among cT1 and cT1a tumors was higher in the malignant group (P < .001, for each). At a cutoff value of 97.50 Hounsfield Units, the peritumoral adipose tissue density had a sensitivity of 83.0% and a specificity of 79.2% for predicting the presence of malignant tumors in ≤7 cm renal masses. Using a cutoff value of −97.50 Hounsfield Units, the peritumoral adipose tissue density had a sensitivity of 88.9% and a specificity of 83.3% for predicting the presence of malignant tumors in ≤4 cm renal masses. Conclusion: The peritumoral adipose tissue density in the preoperative computed tomography images predicts the malignancy in cT1 renal masses. Cite this article as: Sahin Y, Aygan S, Hacibey I, et al. Peritumoral adipose tissue density predicts the malignancy in cT1 renal masses. Urol Res Pract. 2023;49(3):191-197.