Comparison of clinical and radiological characteristics of inflammatory and non-inflammatory Rathke cleft cysts.

PURPOSE: Rathke cleft cysts are commonly encountered sellar lesions, and their inflammation induces symptoms and recurrence. Cyst wall enhancement is related to inflammation; however, its range and frequency have not yet been investigated. This study aimed to investigate the clinical and radiological differences between inflammatory and non-inflammatory Rathke cleft cysts. METHODS: Forty-one patients who underwent cyst decompression surgery for Rathke's cleft cysts between January 2008 and July 2022 were retrospectively analyzed. Based on the pathological reports, patients were divided into inflammatory and non-inflammatory groups. Clinical assessments, endocrinological evaluations, cyst content analysis, and imaging metrics (mean computed tomographic value, maximum diameter, mean apparent diffusion coefficient [ADC] value, and qualitative features) were analyzed. Receiver operating characteristic curve analysis was performed, to determine ADC cutoff values, for differentiating inflammatory group from non-inflammatory group. RESULTS: Totally, 21 and 20 cases were categorized into the inflammatory and non-inflammatory groups, respectively. The inflammatory group displayed a higher incidence of central diabetes insipidus (arginine vasopressin deficiency) (p = 0.04), turbid cyst content (p = 0.03), significantly lower mean ADC values (p = 0.04), and more extensive circumferential wall enhancement on magnetic resonance imaging (MRI) (p < 0.001). In the inflammatory group, all cases revealed circumferential wall enhancement, with some exhibiting thick wall enhancement. There were no significant differences in other radiological features. The ADC cutoff value for differentiating the two groups was 1.57 × 10-3 mm2/s, showing a sensitivity of 81.3% and specificity of 66.7% CONCLUSION: Inflammatory Rathke cleft cysts tended to show a higher incidence of central diabetes insipidus and turbid cyst content. Radiologically, they exhibited lower mean ADC values and greater circumferential wall enhancement on MRI.

Comparative analysis of GPT-4-based ChatGPT's diagnostic performance with radiologists using real-world radiology reports of brain tumors.

OBJECTIVES: Large language models like GPT-4 have demonstrated potential for diagnosis in radiology. Previous studies investigating this potential primarily utilized quizzes from academic journals. This study aimed to assess the diagnostic capabilities of GPT-4-based Chat Generative Pre-trained Transformer (ChatGPT) using actual clinical radiology reports of brain tumors and compare its performance with that of neuroradiologists and general radiologists. METHODS: We collected brain MRI reports written in Japanese from preoperative brain tumor patients at two institutions from January 2017 to December 2021. The MRI reports were translated into English by radiologists. GPT-4 and five radiologists were presented with the same textual findings from the reports and asked to suggest differential and final diagnoses. The pathological diagnosis of the excised tumor served as the ground truth. McNemar's test and Fisher's exact test were used for statistical analysis. RESULTS: In a study analyzing 150 radiological reports, GPT-4 achieved a final diagnostic accuracy of 73%, while radiologists' accuracy ranged from 65 to 79%. GPT-4's final diagnostic accuracy using reports from neuroradiologists was higher at 80%, compared to 60% using those from general radiologists. In the realm of differential diagnoses, GPT-4's accuracy was 94%, while radiologists' fell between 73 and 89%. Notably, for these differential diagnoses, GPT-4's accuracy remained consistent whether reports were from neuroradiologists or general radiologists. CONCLUSION: GPT-4 exhibited good diagnostic capability, comparable to neuroradiologists in differentiating brain tumors from MRI reports. GPT-4 can be a second opinion for neuroradiologists on final diagnoses and a guidance tool for general radiologists and residents. CLINICAL RELEVANCE STATEMENT: This study evaluated GPT-4-based ChatGPT's diagnostic capabilities using real-world clinical MRI reports from brain tumor cases, revealing that its accuracy in interpreting brain tumors from MRI findings is competitive with radiologists. KEY POINTS: We investigated the diagnostic accuracy of GPT-4 using real-world clinical MRI reports of brain tumors. GPT-4 achieved final and differential diagnostic accuracy that is comparable with neuroradiologists. GPT-4 has the potential to improve the diagnostic process in clinical radiology.

Evaluation of cranial nerve involvement in chordomas and chondrosarcomas: a retrospective imaging study.

PURPOSE: Cranial nerve involvement (CNI) influences the treatment strategies and prognosis of head and neck tumors. However, its incidence in skull base chordomas and chondrosarcomas remains to be investigated. This study evaluated the imaging features of chordoma and chondrosarcoma, with a focus on the differences in CNI. METHODS: Forty-two patients (26 and 16 patients with chordomas and chondrosarcomas, respectively) treated at our institution between January 2007 and January 2023 were included in this retrospective study. Imaging features, such as the maximum diameter, tumor location (midline or off-midline), calcification, signal intensity on T2-weighted image, mean apparent diffusion coefficient (ADC) values, contrast enhancement, and CNI, were evaluated and compared using Fisher's exact test or the Mann-Whitney U-test. The odds ratio (OR) was calculated to evaluate the association between the histological type and imaging features. RESULTS: The incidence of CNI in chondrosarcomas was significantly higher than that in chordomas (63% vs. 8%, P < 0.001). An off-midline location was more common in chondrosarcomas than in chordomas (86% vs. 13%; P < 0.001). The mean ADC values of chondrosarcomas were significantly higher than those of chordomas (P < 0.001). Significant associations were identified between chondrosarcomas and CNI (OR = 20.00; P < 0.001), location (OR = 53.70; P < 0.001), and mean ADC values (OR = 1.01; P = 0.002). CONCLUSION: The incidence of CNI and off-midline location in chondrosarcomas was significantly higher than that in chordomas. CNI, tumor location, and the mean ADC can help distinguish between these entities.

Accuracy of ChatGPT generated diagnosis from patient's medical history and imaging findings in neuroradiology cases.

PURPOSE: The noteworthy performance of Chat Generative Pre-trained Transformer (ChatGPT), an artificial intelligence text generation model based on the GPT-4 architecture, has been demonstrated in various fields; however, its potential applications in neuroradiology remain unexplored. This study aimed to evaluate the diagnostic performance of GPT-4 based ChatGPT in neuroradiology. METHODS: We collected 100 consecutive "Case of the Week" cases from the American Journal of Neuroradiology between October 2021 and September 2023. ChatGPT generated a diagnosis from patient's medical history and imaging findings for each case. Then the diagnostic accuracy rate was determined using the published ground truth. Each case was categorized by anatomical location (brain, spine, and head & neck), and brain cases were further divided into central nervous system (CNS) tumor and non-CNS tumor groups. Fisher's exact test was conducted to compare the accuracy rates among the three anatomical locations, as well as between the CNS tumor and non-CNS tumor groups. RESULTS: ChatGPT achieved a diagnostic accuracy rate of 50% (50/100 cases). There were no significant differences between the accuracy rates of the three anatomical locations (p = 0.89). The accuracy rate was significantly lower for the CNS tumor group compared to the non-CNS tumor group in the brain cases (16% [3/19] vs. 62% [36/58], p < 0.001). CONCLUSION: This study demonstrated the diagnostic performance of ChatGPT in neuroradiology. ChatGPT's diagnostic accuracy varied depending on disease etiologies, and its diagnostic accuracy was significantly lower in CNS tumors compared to non-CNS tumors.

AI-based Virtual Synthesis of Methionine PET from Contrast-enhanced MRI: Development and External Validation Study.

Background Carbon 11 (11C)-methionine is a useful PET radiotracer for the management of patients with glioma, but radiation exposure and lack of molecular imaging facilities limit its use. Purpose To generate synthetic methionine PET images from contrast-enhanced (CE) MRI through an artificial intelligence (AI)-based image-to-image translation model and to compare its performance for grading and prognosis of gliomas with that of real PET. Materials and Methods An AI-based model to generate synthetic methionine PET images from CE MRI was developed and validated from patients who underwent both methionine PET and CE MRI at a university hospital from January 2007 to December 2018 (institutional data set). Pearson correlation coefficients for the maximum and mean tumor to background ratio (TBRmax and TBRmean, respectively) of methionine uptake and the lesion volume between synthetic and real PET were calculated. Two additional open-source glioma databases of preoperative CE MRI without methionine PET were used as the external test set. Using the TBRs, the area under the receiver operating characteristic curve (AUC) for classifying high-grade and low-grade gliomas and overall survival were evaluated. Results The institutional data set included 362 patients (mean age, 49 years ± 19 [SD]; 195 female, 167 male; training, n = 294; validation, n = 34; test, n = 34). In the internal test set, Pearson correlation coefficients were 0.68 (95% CI: 0.47, 0.81), 0.76 (95% CI: 0.59, 0.86), and 0.92 (95% CI: 0.85, 0.95) for TBRmax, TBRmean, and lesion volume, respectively. The external test set included 344 patients with gliomas (mean age, 53 years ± 15; 192 male, 152 female; high grade, n = 269). The AUC for TBRmax was 0.81 (95% CI: 0.75, 0.86) and the overall survival analysis showed a significant difference between the high (2-year survival rate, 27%) and low (2-year survival rate, 71%; P < .001) TBRmax groups. Conclusion The AI-based model-generated synthetic methionine PET images strongly correlated with real PET images and showed good performance for glioma grading and prognostication. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Frequency and imaging features of the adjacent osseous changes of salivary gland carcinomas in the head and neck region.

PURPOSE: The association between salivary gland carcinomas and adjacent osseous changes in the head and neck region is not clear. We evaluated the frequency and imaging features of such changes and investigated the specific characteristics of salivary gland carcinomas associated with them. METHODS: A total of 118 patients with histologically proven salivary gland carcinomas were retrospectively reviewed. The imaging characteristics of osseous changes were sorted into three categories based on computed tomography images: sclerotic change, erosive change, and lytic change. The frequency of all these osseous changes and any one of them was compared between different pathologies using Fisher's exact test. Odds ratios were calculated to evaluate the association between these changes and perineural spread. RESULTS: Osseous changes were found in 21 (18%) of 118 cases. Among these, seven (6%) cases were with sclerotic, nine (8%) with erosive, and nine (8%) with lytic changes (four with mixed change). Adenoid cystic carcinoma showed a significantly higher frequency of sclerotic and erosive changes, and either osseous change, than the other salivary gland carcinomas (p < 0.001 for each). Sclerotic changes were only present in the adenoid cystic carcinomas. Perineural spread was a significant factor in showing higher osseous change frequencies (odds ratio = 3.98, p = 0.006). CONCLUSION: Among salivary gland carcinomas in the head and neck region, adenoid cystic carcinomas had a significantly higher frequency of adjacent osseous changes, especially sclerotic changes, than other salivary gland carcinomas.

Orbital apex schwannoma with a high titer of proteinase 3 antineutrophil cytoplasmic antibody.

Here, we present a very unusual case of orbital apex schwannoma with a high titer of proteinase 3 antineutrophil cytoplasmic antibody (PR3-ANCA). A 67-year-old man presented with a 3-month history of double vision. Radiological examinations revealed a mass lesion at the left orbital apex, and laboratory examination revealed a high titer of PR3-ANCA, of 49.1 U/mL (reference range<2.0). After the surgery, the lesion was histologically diagnosed as schwannoma, and the PR3-ANCA titer decreased to 8.4 U/m. Although making a correct diagnosis of orbital apex schwannoma may be difficult due to the need to differentiate from granulomatosis with polyangiitis when PR3-ANCA serum levels are elevated, careful examination of the radiological findings may aid the diagnosis.

Malignant transformation of a dysembryoplastic neuroepithelial tumor presenting with intraventricular hemorrhage.

Dysembryoplastic neuroepithelial tumors (DNTs) are benign brain tumors classified as grade 1 in the 2021 World Health Organization (WHO) classification of central nervous system tumors. DNTs rarely undergo malignant transformation and cause symptomatic intracranial hemorrhage. We report a case of malignant transformation of DNT presenting with intraventricular hemorrhage and review the literature on malignant transformation of DNTs. An 18-year-old woman with a history of epilepsy presented with a sudden headache and vomiting. Radiological examination revealed a mass lesion in the left parietal lobe and intraventricular hemorrhage. The patient underwent an emergency craniotomy for brain tumor resection. The lesion was pathologically diagnosed as a malignant transformation of DNT. She had been followed up without tumor recurrence for 2 years after surgery.

Cystic lesions of the parotid gland: radiologic-pathologic correlation according to the latest World Health Organization 2017 Classification of Head and Neck Tumours.

The latest World Health Organization 2017 Classification of Head and Neck Tumours includes a new chapter on tumors and tumor-like lesions of the neck and lymph nodes. Tumor-like lesions include a variety of cystic lesions of the parotid gland. Cystic lesions of the parotid gland can be divided into three groups: non-neoplastic cysts, benign tumors with macrocystic change, and malignant tumors with macrocystic change. It is important to distinguish these lesions from one another because treatment and patient management differ among the three groups. The purpose of this review is to describe the magnetic resonance imaging and scintigraphy findings and the histopathologic characteristics of each parotid gland lesion based on the latest World Health Organization 2017 Classification of Head and Neck Tumours and to summarize the key points of differential diagnosis for cystic lesions of the parotid gland.