Optimizing Diagnostic Performance of ChatGPT: The Impact of Prompt Engineering on Thoracic Radiology Cases.

Background Recent studies have highlighted the diagnostic performance of ChatGPT 3.5 and GPT-4 in a text-based format, demonstrating their radiological knowledge across different areas. Our objective is to investigate the impact of prompt engineering on the diagnostic performance of ChatGPT 3.5 and GPT-4 in diagnosing thoracic radiology cases, highlighting how the complexity of prompts influences model performance. Methodology We conducted a retrospective cross-sectional study using 124 publicly available Case of the Month examples from the Thoracic Society of Radiology website. We initially input the cases into the ChatGPT versions without prompting. Then, we employed five different prompts, ranging from basic task-oriented to complex role-specific formulations to measure the diagnostic accuracy of ChatGPT versions. The differential diagnosis lists generated by the models were compared against the radiological diagnoses listed on the Thoracic Society of Radiology website, with a scoring system in place to comprehensively assess the accuracy. Diagnostic accuracy and differential diagnosis scores were analyzed using the McNemar, Chi-square, Kruskal-Wallis, and Mann-Whitney U tests. Results Without any prompts, ChatGPT 3.5's accuracy was 25% (31/124), which increased to 56.5% (70/124) with the most complex prompt (P < 0.001). GPT-4 showed a high baseline accuracy at 53.2% (66/124) without prompting. This accuracy increased to 59.7% (74/124) with complex prompts (P = 0.09). Notably, there was no statistical difference in peak performance between ChatGPT 3.5 (70/124) and GPT-4 (74/124) (P = 0.55). Conclusions This study emphasizes the critical influence of prompt engineering on enhancing the diagnostic performance of ChatGPT versions, especially ChatGPT 3.5.

Primary brain lymphoma and glioblastoma: evaluation of DCE T1 and DSC T2 MRI perfusion findings.

BACKGROUND: The accurate differentiation of primary central nervous system lymphoma (PCNSL) from glioblastoma multiforme (GBM) is clinically crucial due to the different treatment strategies between them. PURPOSE: To define magnetic resonance imaging (MRI) perfusion findings in PCNSL to make a safe distinction from GBM with dynamic contrast-enhanced (DCE) T1 and DSC T2 MRI perfusion findings. MATERIAL AND METHODS: This retrospective analysis included 19 patients with histopathologically diagnosed PCNSL and 21 individuals with GBM. DCE T1 vascular permeability perfusion values including K-trans, Ve, Kep, IAUGC, and DSC T2 perfusion values including cerebral blood volume (CBV) and cerebral blood flow (CBF) in axial sections from the pathological lesion and contralateral normal brain parenchyma were measured quantitatively using region of interest analysis. RESULTS: The study observed no statistically significant difference between patients with PCNSL (T/B cell) and GBM in the median values of DCE T1 perfusion ratios (P > 0.05). Nevertheless, the DSC T2 perfusion ratios showed a substantial distinction between the two groups. In contrast to patients with PCNSL (1.185 vs. 1.224, respectively), those with GBM had higher median levels of r-CBV and r-CBF (2.898 vs. 2.467, respectively; P 0.01). A cutoff value of ≤1.473 for r-CBV (Lesion/N) and ≤1.6005 for r-CBF (Lesion/N) was found to estimate the positivity of PCNSL. CONCLUSION: DSC T2 MRI perfusion values showed lower r-CBV and r-CBF values in PCNSL patients compared to GBM patients. According to the findings, r-CBV and r-CBF are the most accurate MRI perfusion parameters for distinguishing between PCSNL and GBM.

MRI in the Diagnosis of Bucket Handle Tears: What Is the Current Situation?

Introduction The purpose of this study was to determine the utility of current magnetic resonance imaging (MRI) in the diagnosis of bucket-handle meniscal tears. Materials and methods Patients treated for arthroscopic meniscal tears between March 2019 and March 2022 were reviewed. The current study included all patients with bucket handle tears diagnosed arthroscopically and having MRI scans (n=51). A control group of 58 individuals with similar demographic characteristics and meniscal tears apart from bucket handle tears was also formed. The assessment of bucket handle and non-bucket handle tears was performed blindly by a musculoskeletal (MSK) radiologist with 20 years of experience and a trainee radiologist, achieving consensus on group allocation. The MRIs were examined for various findings, including the presence of a bucket handle tear, tear location, presence of anterior cruciate ligament (ACL) rupture, intercondyler notch sign, double anterior horn sign, flipped meniscus sign, double posterior cruciate ligament (PCL) sign, absent bow sign, and the disproportionate posterior horn sign. These well-known signs, detailed in the literature, were evaluated. Additionally, less studied and less commonly known signs such as the V sign and double anterior cruciate ligament sign were assessed. The V sign appears similarly to the letter V, resulting from the displacement of the bucket handle tear and the angle of the intact meniscus on axial images. The double anterior cruciate ligament sign is the appearance formed by the compression of the displaced meniscal part behind the anterior cruciate ligament in bucket handle tears. Results Following the retrospective evaluation of MRI scans, 44 out of 51 tears diagnosed as bucket handle tears by arthroscopy were accurately identified (sensitivity: 86.27%). The same conclusion was reached for MRI scans in 52 out of 58 tears where arthroscopy did not detect a bucket handle tear (specificity: 89.66%). The most prevalent MRI signs in patients with bucket handle tears identified by arthroscopy in the study were the intercondylar notch sign (84.31%), V sign (72.55%), double PCL sign (56.86%), double anterior horn sign (49.02%), absent bow sign (43.14%), flipped meniscus sign (19.61%), disproportionate posterior horn sign (9.80%), and double ACL sign (5.88%). The intercondylar notch sign, V sign, and double PCL sign exhibited the highest sensitivity, while flipped meniscus, disproportionate posterior horn, and double ACL sign demonstrated the highest specificity. Conclusion MRI demonstrates a high level of sensitivity and specificity in identifying meniscal bucket handle tears, particularly when considering the eight MRI signs investigated in this study.