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BACKGROUND: Although brain activities in Alzheimer's disease (AD) might be evaluated MRI and PET, the relationships between brain temperature (BT), the index of diffusivity along the perivascular space (ALPS index), and amyloid deposition in the cerebral cortex are still unclear. PURPOSE: To investigate the relationship between metabolic imaging measurements and clinical information in patients with AD and normal controls (NCs). STUDY TYPE: Retrospective analysis of a prospective dataset. POPULATION: 58 participants (78.3 ± 6.8 years; 30 female): 29 AD patients and 29 age- and sex-matched NCs from the Open Access Series of Imaging Studies dataset. FIELD STRENGTH/SEQUENCE: 3T; T1-weighted magnetization-prepared rapid gradient-echo, diffusion tensor imaging with 64 directions, and dynamic 18 F-florbetapir PET. ASSESSMENT: Imaging metrics were compared between AD and NCs. These included BT calculated by the diffusivity of the lateral ventricles, ALPS index that reflects the glymphatic system, the mean standardized uptake value ratio (SUVR) of amyloid PET in the cerebral cortex and clinical information, such as age, sex, and MMSE. STATISTICAL TESTS: Pearson's or Spearman's correlation and multiple linear regression analyses. P values <0.05 were defined as statistically significant. RESULTS: Significant positive correlations were found between BT and ALPS index (r = 0.44 for NCs), while significant negative correlations were found between age and ALPS index (rs = -0.43 for AD and - 0.47 for NCs). The SUVR of amyloid PET was not significantly associated with BT (P = 0.81 for AD and 0.21 for NCs) or ALPS index (P = 0.10 for AD and 0.52 for NCs). In the multiple regression analysis, age was significantly associated with BT, while age, sex, and presence of AD were significantly associated with the ALPS index. DATA CONCLUSION: Impairment of the glymphatic system measured using MRI was associated with lower BT and aging. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 1.
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Enfermedad de Alzheimer , Humanos , Femenino , Enfermedad de Alzheimer/diagnóstico por imagen , Enfermedad de Alzheimer/metabolismo , Imagen de Difusión Tensora/métodos , Estudios Retrospectivos , Estudios Prospectivos , Acceso a la Información , Tomografía de Emisión de Positrones/métodos , Imagen por Resonancia Magnética/métodos , Amiloide , Proteínas Amiloidogénicas , Corteza CerebralRESUMEN
OBJECTIVES: To compare the diagnostic accuracy of Generative Pre-trained Transformer (GPT)-4-based ChatGPT, GPT-4 with vision (GPT-4V) based ChatGPT, and radiologists in musculoskeletal radiology. MATERIALS AND METHODS: We included 106 "Test Yourself" cases from Skeletal Radiology between January 2014 and September 2023. We input the medical history and imaging findings into GPT-4-based ChatGPT and the medical history and images into GPT-4V-based ChatGPT, then both generated a diagnosis for each case. Two radiologists (a radiology resident and a board-certified radiologist) independently provided diagnoses for all cases. The diagnostic accuracy rates were determined based on the published ground truth. Chi-square tests were performed to compare the diagnostic accuracy of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and radiologists. RESULTS: GPT-4-based ChatGPT significantly outperformed GPT-4V-based ChatGPT (p < 0.001) with accuracy rates of 43% (46/106) and 8% (9/106), respectively. The radiology resident and the board-certified radiologist achieved accuracy rates of 41% (43/106) and 53% (56/106). The diagnostic accuracy of GPT-4-based ChatGPT was comparable to that of the radiology resident, but was lower than that of the board-certified radiologist although the differences were not significant (p = 0.78 and 0.22, respectively). The diagnostic accuracy of GPT-4V-based ChatGPT was significantly lower than those of both radiologists (p < 0.001 and < 0.001, respectively). CONCLUSION: GPT-4-based ChatGPT demonstrated significantly higher diagnostic accuracy than GPT-4V-based ChatGPT. While GPT-4-based ChatGPT's diagnostic performance was comparable to radiology residents, it did not reach the performance level of board-certified radiologists in musculoskeletal radiology. CLINICAL RELEVANCE STATEMENT: GPT-4-based ChatGPT outperformed GPT-4V-based ChatGPT and was comparable to radiology residents, but it did not reach the level of board-certified radiologists in musculoskeletal radiology. Radiologists should comprehend ChatGPT's current performance as a diagnostic tool for optimal utilization. KEY POINTS: This study compared the diagnostic performance of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and radiologists in musculoskeletal radiology. GPT-4-based ChatGPT was comparable to radiology residents, but did not reach the level of board-certified radiologists. When utilizing ChatGPT, it is crucial to input appropriate descriptions of imaging findings rather than the images.
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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.
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Inteligencia Artificial , Neoplasias , Humanos , Cabeza , Encéfalo , CuelloRESUMEN
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.
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Condrosarcoma , Cordoma , Neoplasias de la Base del Cráneo , Humanos , Femenino , Masculino , Estudios Retrospectivos , Persona de Mediana Edad , Cordoma/diagnóstico por imagen , Cordoma/patología , Adulto , Condrosarcoma/diagnóstico por imagen , Condrosarcoma/patología , Anciano , Neoplasias de la Base del Cráneo/diagnóstico por imagen , Medios de Contraste , Adolescente , Imagen por Resonancia Magnética/métodosRESUMEN
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.
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Neoplasias Encefálicas , Glioma , Humanos , Masculino , Femenino , Persona de Mediana Edad , Metionina , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/patología , Inteligencia Artificial , Tomografía de Emisión de Positrones/métodos , Clasificación del Tumor , Glioma/diagnóstico por imagen , Glioma/patología , Imagen por Resonancia Magnética/métodos , RacemetioninaRESUMEN
BACKGROUND: Functional seizures (FS) are paroxysmal episodes, resembling epileptic seizures, but without underlying epileptic abnormality. The aetiology and neuroanatomic associations are incompletely understood. Recent brain imaging data indicate cerebral changes, however, without clarifying possible pathophysiology. In the present study, we specifically investigated the neuroanatomic changes in subregions of the amygdala and hippocampus in FS. METHODS: T1 MRI scans of 37 female patients with FS and 37 age-matched female seizure naïve controls (SNC) were analyzed retrospectively in FreeSurfer version 7.1. Seizure naïve controls included patients with depression and anxiety disorders. The analysis included whole-brain cortical thickness, subcortical volumes, and subfields of the amygdala and hippocampus. Group comparisons were carried out using multivariable linear models. RESULTS: The FS and SNC groups did not differ in the whole hippocampus and amygdala volumes. However, patients had a significant reduction of the right lateral amygdala volume (p = 0.00041), an increase of the right central amygdala, (p = 0.037), and thinning of the left superior frontal gyrus (p = 0.024). Additional findings in patients were increased volumes of the right medial amygdala (p = 0.031), left anterior amygdala (p = 0.017), and left dentate gyrus of the hippocampus (p = 0.035). CONCLUSIONS: The observations from the amygdala and hippocampus segmentation affirm that there are neuroanatomic associations of FS. The pattern of these changes aligned with some of the cerebral changes described in chronic stress conditions and depression. The pattern of detected changes further study, and may, after validation, provide biomarkers for diagnosis and treatment.
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Amígdala del Cerebelo , Epilepsia , Humanos , Femenino , Estudios Retrospectivos , Amígdala del Cerebelo/diagnóstico por imagen , Corteza Prefrontal/diagnóstico por imagen , Hipocampo/diagnóstico por imagen , Convulsiones/diagnóstico por imagen , Imagen por Resonancia Magnética/métodosRESUMEN
PURPOSE: To investigate the daily fluctuations in brain temperature in healthy individuals using magnetic resonance (MR) diffusion-weighted imaging (DWI) thermometry and to clarify the associations between the brain and body temperatures and sex. METHODS: Thirty-two age-matched healthy male and female volunteers (male = 16, 20-38 years) were recruited between July 2021 and January 2022. Brain MR examinations were performed in the morning and evening phases on the same day to calculate the brain temperatures using DWI thermometry. Body temperature was also measured in each MR examination. Group comparisons of body and brain temperatures between the two phases were performed using paired t-tests. A multiple linear regression model was used to predict the morning brain temperature using sex, evening brain temperature, and the interaction between sex and evening brain temperature as covariates. RESULTS: Body temperatures were significantly higher in the evening than in the morning in all participants, male group, and female group (p < 0.001, = 0001, and < 0.001, respectively). Meanwhile, no significant difference was observed between the morning and evening brain temperatures in each analysis (p = 0.23, 0.70, and 0.16, respectively). Multiple linear regression analysis showed significant associations of morning brain temperature with sex (p = 0.038), evening brain temperature (p < 0.001), and the interaction between sex and evening brain temperature (p = 0.036). CONCLUSION: Unlike body temperature, brain temperature showed no significant daily fluctuations; however, daily fluctuations in brain temperature may vary depending on sex.
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Temperatura Corporal , Termometría , Masculino , Humanos , Femenino , Temperatura , Encéfalo/diagnóstico por imagen , Termometría/métodos , Imagen de Difusión por Resonancia Magnética/métodosRESUMEN
PURPOSE: Pseudoprogression (PsP) remains an elusive and clinically important, yet ill-defined, phenomena that, generally, involves a period of early radiographic progression (enhancement) followed by a period of radiographic stability or regression. In the current study, we utilized data from the control arm of a phase III clinical trial in newly-diagnosed glioblastoma to explore imaging characteristics of "clinically-defined PsP", or early radiographic progression (PFS < 6 months from chemoradiation) followed by a long post-progression residual overall survival (ROS > 12 months). METHODS: One hundred sixty-nine patients with newly-diagnosed GBM from the control arm of the AVAglio trial (NCT00943826) who presented with early radiographic progressive disease (PD) (< 6 months) were included. Clinical characteristics, topographical patterns, and radiomic features were compared between newly-diagnosed GBM exhibiting early PD and early death (< 12-month ROS, "true PD") with those exhibiting early PD and a long residual survival (> 12-month ROS, "clinically-defined PsP"). RESULTS: "Clinically-defined PsP" occurred to 38.5% of patients with early PD, and was more associated with MGMT methylation (P = 0.02), younger age (P = 0.003), better neurological performance (P = 0.01), and lower contrast-enhancing tumor volume (P = 0.002) at baseline. GBM showing "true PD" occurred more frequently in the right internal capsule, thalamus, lentiform nucleus, and temporal lobe than those with "clinical PsP". Radiomic analysis predicted "clinical PsP" with > 70% accuracy on the validation dataset. CONCLUSION: Patients with early PD that eventually exhibit "clinically-defined PsP" have distinct clinical, molecular, and MRI characteristics. This information may be useful for treating clinicians to better understand the potential risks and outcome in patients exhibiting early radiographic changes following chemoradiation.
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Neoplasias Encefálicas , Glioblastoma , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/terapia , Quimioradioterapia/métodos , Progresión de la Enfermedad , Glioblastoma/diagnóstico por imagen , Glioblastoma/terapia , Humanos , Incidencia , Imagen por Resonancia Magnética , Especies Reactivas de OxígenoRESUMEN
PURPOSE: Functional seizures (FS), also known as psychogenic nonepileptic seizures (PNES), are physical manifestations of acute or chronic psychological distress. Functional and structural neuroimaging have identified objective signs of this disorder. We evaluated whether magnetic resonance imaging (MRI) morphometry differed between patients with FS and clinically relevant comparison populations. METHODS: Quality-screened clinical-grade MRIs were acquired from 666 patients from 2006 to 2020. Morphometric features were quantified with FreeSurfer v6. Mixed-effects linear regression compared the volume, thickness, and surface area within 201 regions-of-interest for 90 patients with FS, compared to seizure-naïve patients with depression (n = 243), anxiety (n = 68), and obsessive-compulsive disorder (OCD, n = 41), respectively, and to other seizure-naïve controls with similar quality MRIs, accounting for the influence of multiple confounds including depression and anxiety based on chart review. These comparison populations were obtained through review of clinical records plus research studies obtained on similar scanners. RESULTS: After Bonferroni-Holm correction, patients with FS compared with seizure-naïve controls exhibited thinner bilateral superior temporal cortex (left 0.053 mm, p = 0.014; right 0.071 mm, p = 0.00006), thicker left lateral occipital cortex (0.052 mm, p = 0.0035), and greater left cerebellar white-matter volume (1085 mm3, p = 0.0065). These findings were not accounted for by lower MRI quality in patients with FS. CONCLUSIONS: These results reinforce prior indications of structural neuroimaging correlates of FS and, in particular, distinguish brain morphology in FS from that in depression, anxiety, and OCD. Future work may entail comparisons with other psychiatric disorders including bipolar and schizophrenia, as well as exploration of brain structural heterogeneity within FS.
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Imagen por Resonancia Magnética , Trastorno Obsesivo Compulsivo , Encéfalo , Humanos , Neuroimagen , ConvulsionesRESUMEN
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.
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Carcinoma Adenoide Quístico , Neoplasias de las Glándulas Salivales , Carcinoma Adenoide Quístico/diagnóstico por imagen , Carcinoma Adenoide Quístico/patología , Humanos , Cuello/patología , Estudios Retrospectivos , Neoplasias de las Glándulas Salivales/diagnóstico por imagen , Neoplasias de las Glándulas Salivales/patología , Glándulas Salivales/patologíaRESUMEN
Background Digital subtraction angiography (DSA) generates an image by subtracting a mask image from a dynamic angiogram. However, patient movement-caused misregistration artifacts can result in unclear DSA images that interrupt procedures. Purpose To train and to validate a deep learning (DL)-based model to produce DSA-like cerebral angiograms directly from dynamic angiograms and then quantitatively and visually evaluate these angiograms for clinical usefulness. Materials and Methods A retrospective model development and validation study was conducted on dynamic and DSA image pairs consecutively collected from January 2019 through April 2019. Angiograms showing misregistration were first separated per patient by two radiologists and sorted into the misregistration test data set. Nonmisregistration angiograms were divided into development and external test data sets at a ratio of 8:1 per patient. The development data set was divided into training and validation data sets at ratio of 3:1 per patient. The DL model was created by using the training data set, tuned with the validation data set, and then evaluated quantitatively with the external test data set and visually with the misregistration test data set. Quantitative evaluations used the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) with mixed liner models. Visual evaluation was conducted by using a numerical rating scale. Results The training, validation, nonmisregistration test, and misregistration test data sets included 10 751, 2784, 1346, and 711 paired images collected from 40 patients (mean age, 62 years ± 11 [standard deviation]; 33 women). In the quantitative evaluation, DL-generated angiograms showed a mean PSNR value of 40.2 dB ± 4.05 and a mean SSIM value of 0.97 ± 0.02, indicating high coincidence with the paired DSA images. In the visual evaluation, the median ratings of the DL-generated angiograms were similar to or better than those of the original DSA images for all 24 sequences. Conclusion The deep learning-based model provided clinically useful cerebral angiograms free from clinically significant artifacts directly from dynamic angiograms. Published under a CC BY 4.0 license. Supplemental material is available for this article.
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Angiografía Cerebral , Aprendizaje Profundo , Aumento de la Imagen/métodos , Adulto , Anciano , Anciano de 80 o más Años , Angiografía de Substracción Digital , Artefactos , Femenino , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Masculino , Persona de Mediana Edad , Estudios Retrospectivos , Relación Señal-RuidoRESUMEN
PURPOSE: Although tumor localization and 3,4-dihydroxy-6-18F-fluoro-L-phenylalanine (FDOPA) uptake may have an association, preferential tumor localization in relation to FDOPA uptake is yet to be investigated in lower-grade gliomas (LGGs). This study aimed to identify differences in the frequency of tumor localization between FDOPA hypometabolic and hypermetabolic LGGs using a probabilistic radiographic atlas. METHODS: Fifty-one patients with newly diagnosed LGG (WHO grade II, 29; III, 22; isocitrate dehydrogenase wild-type, 21; mutant 1p19q non-codeleted,16; mutant codeleted, 14) who underwent FDOPA positron emission tomography (PET) were retrospectively selected. Semiautomated tumor segmentation on FLAIR was performed. Patients with LGGs were separated into two groups (FDOPA hypometabolic and hypermetabolic LGGs) according to the normalized maximum standardized uptake value of FDOPA PET (a threshold of the uptake in the striatum) within the segmented regions. Spatial normalization procedures to build a 3D MRI-based atlas using each segmented region were validated by an analysis of differential involvement statistical mapping. RESULTS: Superimposition of regions of interest showed a high number of hypometabolic LGGs localized in the frontal lobe, while a high number of hypermetabolic LGGs was localized in the insula, putamen, and temporal lobe. The statistical mapping revealed that hypometabolic LGGs occurred more frequently in the superior frontal gyrus (close to the supplementary motor area), while hypermetabolic LGGs occurred more frequently in the insula. CONCLUSION: Radiographic atlases revealed preferential frontal lobe localization for FDOPA hypometabolic LGGs, which may be associated with relatively early detection.
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Neoplasias Encefálicas , Glioma , Neoplasias Encefálicas/diagnóstico por imagen , Dihidroxifenilalanina , Glioma/diagnóstico por imagen , Humanos , Isocitrato Deshidrogenasa , Clasificación del Tumor , Tomografía de Emisión de Positrones , Estudios RetrospectivosRESUMEN
PURPOSE: To examine whether the rate of change in maximum 18F-FDOPA PET uptake and the rate of change in non-enhancing tumor volume could predict malignant transformation and residual overall survival (OS) in low grade glioma (LGG) patients who received serial 18F-FDOPA PET and MRI scans. METHODS: 27 LGG patients with ≥ 2 18F-FDOPA PET and MRI scans between 2003 and 2016 were included. The rate of change in FLAIR volume (uL/day) and maximum normalized 18F-FDOPA specific uptake value (nSUVmax/month), were compared between histological and molecular subtypes. General linear models (GLMs) were used to integrate clinical information with MR-PET measurements to predict malignant transformation. Cox univariate and multivariable regression analyses were performed to identify imaging and clinical risk factors related to OS. RESULTS: A GLM using patient age, treatment, the rate of change in FLAIR and 18F-FDOPA nSUVmax could predict malignant transformation with > 67% sensitivity and specificity (AUC = 0.7556, P = 0.0248). A significant association was observed between OS and continuous rates of change in PET uptake (HR = 1.0212, P = 0.0034). Cox multivariable analysis confirmed that continuous measures of the rate of change in PET uptake was an independent predictor of OS (HR = 1.0242, P = 0.0033); however, stratification of patients based on increasing or decreasing rate of change in FLAIR (HR = 2.220, P = 0.025), PET uptake (HR = 2.148, P = 0.0311), or both FLAIR and PET (HR = 2.354, P = 0.0135) predicted OS. CONCLUSIONS: The change in maximum normalized 18F-FDOPA PET uptake, with or without clinical information and rate of change in tumor volume, may be useful for predicting the risk of malignant transformation and estimating residual survival in patients with LGG.
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Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/metabolismo , Transformación Celular Neoplásica/metabolismo , Dihidroxifenilalanina/análogos & derivados , Glioma/diagnóstico por imagen , Glioma/metabolismo , Tomografía de Emisión de Positrones , Carga Tumoral , Adulto , Anciano , Anciano de 80 o más Años , Neoplasias Encefálicas/patología , Transformación Celular Neoplásica/patología , Dihidroxifenilalanina/farmacocinética , Femenino , Glioma/patología , Humanos , Imagen por Resonancia Magnética , Masculino , Persona de Mediana Edad , Sensibilidad y Especificidad , Análisis de Supervivencia , Adulto JovenRESUMEN
PURPOSE: To assess whether hypermetabolically-defined regions of interest (ROIs) on 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (FDOPA) positron emission tomography (PET) could be used to evaluate physiological features and whether there are measurable differences between molecular subtypes and tumor grades. METHODS: Sixty-eight treatment-naïve glioma patients who underwent FDOPA PET and magnetic resonance imaging (MRI) were retrospectively included. Fluid-attenuated inversion recovery hyperintense regions (FLAIRROI) were segmented. FDOPA hypermetabolic regions (FDOPAROI, tumor-to-striatum ratios > 1) within FLAIRROI were extracted. Normalized maximum standardized uptake value (nSUVmax), volume of each ROI, and median relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) within FLAIRROI or FDOPAROI were calculated. Imaging metrics were compared using Students t or Mann-Whitney U tests. Area under the curve (AUC) of receiver-operating characteristic curves were used to determine whether imaging metrics within FLAIRROI or FDOPAROI can discriminate different molecular statuses or grades. RESULTS: Using either FLAIRROI or FDOPAROI, the nSUVmax and rCBV were significantly higher and the ADC was lower in isocitrate dehydrogenase (IDH) wild-type than mutant gliomas, and in higher-grade gliomas (HGGs) than lower-grade gliomas (LGGs). The FDOPAROI volume was significantly higher in 1p19q codeleted than non-codeleted gliomas, and in HGGs than LGGs. Although not significant, imaging metrics extracted by FDOPAROI discriminated molecular status and tumor grade more accurately than those extracted by FLAIRROI (AUC of IDH status, 0.87 vs. 0.82; 1p19q status, 0.78 vs. 0.73; grade, 0.87 vs. 0.76). CONCLUSION: FDOPA hypermetabolic ROI may extract useful imaging features of gliomas, which can illuminate biological differences between different molecular status or tumor grades.
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Biomarcadores de Tumor/genética , Neoplasias Encefálicas/patología , Glioma/patología , Imagen por Resonancia Magnética/métodos , Mutación , Tomografía de Emisión de Positrones/métodos , Anciano , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/metabolismo , Femenino , Estudios de Seguimiento , Glioma/genética , Glioma/metabolismo , Humanos , Isocitrato Deshidrogenasa/genética , Masculino , Persona de Mediana Edad , Pronóstico , Curva ROC , Estudios RetrospectivosRESUMEN
BACKGROUND: Intermodality image-to-image translation is an artificial intelligence technique for generating one technique from another. PURPOSE: This review was designed to systematically identify and quantify biases and quality issues preventing validation and clinical application of artificial intelligence models for intermodality image-to-image translation of brain imaging. DATA SOURCES: PubMed, Scopus, and IEEE Xplore were searched through August 2, 2023, for artificial intelligence-based image translation models of radiologic brain images. STUDY SELECTION: This review collected 102 works published between April 2017 and August 2023. DATA ANALYSIS: Eligible studies were evaluated for quality using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and for bias using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Medically-focused article adherence was compared with that of engineering-focused articles overall with the Mann-Whitney U test and for each criterion using the Fisher exact test. DATA SYNTHESIS: Median adherence to the relevant CLAIM criteria was 69% and 38% for PROBAST questions. CLAIM adherence was lower for engineering-focused articles compared with medically-focused articles (65% versus 73%, P < .001). Engineering-focused studies had higher adherence for model description criteria, and medically-focused studies had higher adherence for data set and evaluation descriptions. LIMITATIONS: Our review is limited by the study design and model heterogeneity. CONCLUSIONS: Nearly all studies revealed critical issues preventing clinical application, with engineering-focused studies showing higher adherence for the technical model description but significantly lower overall adherence than medically-focused studies. The pursuit of clinical application requires collaboration from both fields to improve reporting.
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Neuroimagen , Humanos , Neuroimagen/métodos , Neuroimagen/normas , Sesgo , Inteligencia ArtificialRESUMEN
PURPOSE: To compare the diagnostic performance among Generative Pre-trained Transformer (GPT)-4-based ChatGPT, GPT4 with vision (GPT-4V) based ChatGPT, and radiologists in challenging neuroradiology cases. METHODS: We collected 32 consecutive "Freiburg Neuropathology Case Conference" cases from the journal Clinical Neuroradiology between March 2016 and December 2023. We input the medical history and imaging findings into GPT-4-based ChatGPT and the medical history and images into GPT-4V-based ChatGPT, then both generated a diagnosis for each case. Six radiologists (three radiology residents and three board-certified radiologists) independently reviewed all cases and provided diagnoses. ChatGPT and radiologists' diagnostic accuracy rates were evaluated based on the published ground truth. Chi-square tests were performed to compare the diagnostic accuracy of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and radiologists. RESULTS: GPT4 and GPT-4V-based ChatGPTs achieved accuracy rates of 22% (7/32) and 16% (5/32), respectively. Radiologists achieved the following accuracy rates: three radiology residents 28% (9/32), 31% (10/32), and 28% (9/32); and three board-certified radiologists 38% (12/32), 47% (15/32), and 44% (14/32). GPT-4-based ChatGPT's diagnostic accuracy was lower than each radiologist, although not significantly (all pâ¯> 0.07). GPT-4V-based ChatGPT's diagnostic accuracy was also lower than each radiologist and significantly lower than two board-certified radiologists (pâ¯= 0.02 and 0.03) (not significant for radiology residents and one board-certified radiologist [all pâ¯> 0.09]). CONCLUSION: While GPT-4-based ChatGPT demonstrated relatively higher diagnostic performance than GPT-4V-based ChatGPT, the diagnostic performance of GPT4 and GPT-4V-based ChatGPTs did not reach the performance level of either radiology residents or board-certified radiologists in challenging neuroradiology cases.
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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.
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This study created an image-to-image translation model that synthesizes diffusion tensor images (DTI) from conventional diffusion weighted images, and validated the similarities between the original and synthetic DTI. Thirty-two healthy volunteers were prospectively recruited. DTI and DWI were obtained with six and three directions of the motion probing gradient (MPG), respectively. The identical imaging plane was paired for the image-to-image translation model that synthesized one direction of the MPG from DWI. This process was repeated six times in the respective MPG directions. Regions of interest (ROIs) in the lentiform nucleus, thalamus, posterior limb of the internal capsule, posterior thalamic radiation, and splenium of the corpus callosum were created and applied to maps derived from the original and synthetic DTI. The mean values and signal-to-noise ratio (SNR) of the original and synthetic maps for each ROI were compared. The Bland-Altman plot between the original and synthetic data was evaluated. Although the test dataset showed a larger standard deviation of all values and lower SNR in the synthetic data than in the original data, the Bland-Altman plots showed each plot localizing in a similar distribution. Synthetic DTI could be generated from conventional DWI with an image-to-image translation model.