Comparing the Diagnostic Performance of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and Radiologists in Challenging Neuroradiology Cases.

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 p > 0.07). GPT-4V-based ChatGPT's diagnostic accuracy was also lower than each radiologist and significantly lower than two board-certified radiologists (p = 0.02 and 0.03) (not significant for radiology residents and one board-certified radiologist [all p > 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.

Evaluating Biases and Quality Issues in Intermodality Image Translation Studies for Neuroradiology: A Systematic Review.

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.

Deep learning-based diffusion tensor image generation model: a proof-of-concept study.

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.

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.

The Association of Metabolic Brain MRI, Amyloid PET, and Clinical Factors: A Study of Alzheimer's Disease and Normal Controls From the Open Access Series of Imaging Studies Dataset.

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.

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.

Chest radiography as a biomarker of ageing: artificial intelligence-based, multi-institutional model development and validation in Japan.

BACKGROUND: Chest radiographs are widely available and cost-effective; however, their usefulness as a biomarker of ageing using multi-institutional data remains underexplored. The aim of this study was to develop a biomarker of ageing from chest radiography and examine the correlation between the biomarker and diseases. METHODS: In this retrospective, multi-institutional study, we trained, tuned, and externally tested an artificial intelligence (AI) model to estimate the age of healthy individuals using chest radiographs as a biomarker. For the biomarker modelling phase of the study, we used healthy chest radiographs consecutively collected between May 22, 2008, and Dec 28, 2021, from three institutions in Japan. Data from two institutions were used for training, tuning, and internal testing, and data from the third institution were used for external testing. To evaluate the performance of the AI model in estimating ages, we calculated the correlation coefficient, mean square error, root mean square error, and mean absolute error. The correlation investigation phase of the study included chest radiographs from individuals with a known disease that were consecutively collected between Jan 1, 2018, and Dec 31, 2021, from an additional two institutions in Japan. We investigated the odds ratios (ORs) for various diseases given the difference between the AI-estimated age and chronological age (ie, the difference-age). FINDINGS: We included 101 296 chest radiographs from 70 248 participants across five institutions. In the biomarker modelling phase, the external test dataset from 3467 healthy participants included 8046 radiographs. Between the AI-estimated age and chronological age, the correlation coefficient was 0·95 (99% CI 0·95-0·95), the mean square error was 15·0 years (99% CI 14·0-15·0), the root mean square error was 3·8 years (99% CI 3·8-3·9), and the mean absolute error was 3·0 years (99% CI 3·0-3·1). In the correlation investigation phase, the external test datasets from 34 197 participants with a known disease included 34 197 radiographs. The ORs for difference-age were as follows: 1·04 (99% CI 1·04-1·05) for hypertension; 1·02 (1·01-1·03) for hyperuricaemia; 1·05 (1·03-1·06) for chronic obstructive pulmonary disease; 1·08 (1·06-1·09) for interstitial lung disease; 1·05 (1·03-1·06) for chronic renal failure; 1·04 (1·03-1·06) for atrial fibrillation; 1·03 (1·02-1·04) for osteoporosis; and 1·05 (1·03-1·06) for liver cirrhosis. INTERPRETATION: The AI-estimated age using chest radiographs showed a strong correlation with chronological age in the healthy cohorts. Furthermore, in cohorts of individuals with known diseases, the difference between estimated age and chronological age correlated with various chronic diseases. The use of this biomarker might pave the way for enhanced risk stratification methodologies, individualised therapeutic interventions, and innovative early diagnostic and preventive approaches towards age-associated pathologies. FUNDING: None. TRANSLATION: For the Japanese translation of the abstract see Supplementary Materials section.

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.

[MR findings of the inflammatory demyelinating diseases of the central nervous system].

When demyelinating disease of the central nervous system is suspected, MR examination has mainly three roles: diagnosis, imaging biomarkers, and early detection of adverse effects from therapeutic agents. Because the location, size, shape, distribution, signal intensity, and contrast pattern of the brain lesions on MRI vary depending on the demyelinating diseases, careful attentions are required to assess the differential diagnosis and activity. It is necessary to be familiar with not only typical imaging findings but also atypical findings of demyelinating disease since minor neurological findings and nonspecific brain lesions may lead to misdiagnosis of demyelinating disease. This article reviewed the characteristics of MRI findings and showed recent topics of the demyelinating diseases.

Amygdala subfield and prefrontal cortex abnormalities in patients with functional seizures.

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.

Brain temperature remains stable during the day: a study of diffusion-weighted imaging thermometry in healthy individuals.

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.

Correlation between Phase-difference-enhanced MR Imaging and Amyloid Positron Emission Tomography: A Study on Alzheimer's Disease Patients and Normal Controls.

PURPOSE: While amyloid-ß deposition in the cerebral cortex for Alzheimer's disease (AD) is often evaluated by amyloid positron emission tomography (PET), amyloid-ß-related iron can be detected using phase difference enhanced (PADRE) imaging; however, no study has validated the association between PADRE imaging and amyloid PET. This study investigated whether the degree of hypointense areas on PADRE imaging correlated with the uptake of amyloid PET. METHODS: PADRE imaging and amyloid PET were performed in 8 patients with AD and 10 age-matched normal controls. ROIs in the cuneus, precuneus, superior frontal gyrus (SFG), and superior temporal gyrus (STG) were automatically segmented. The degree of hypointense areas on PADRE imaging in each ROI was evaluated using 4-point scaling of visual assessment or volumetric semiquantitative assessment (the percentage of hypointense volume within each ROI). The mean standardized uptake value ratio (SUVR) of amyloid PET in each ROI was also calculated. The Spearman's correlation coefficient between the 4-point scale of PADRE imaging and SUVR of amyloid PET or between the semiquantitative hypointense volume percentage and SUVR in each ROI was evaluated. RESULTS: In the precuneus, a significant positive correlation was identified between the 4-point scale of PADRE imaging and SUVR of amyloid PET (Rs = 0.5; P = 0.034) in all subjects. In the cuneus, a significant positive correlation was identified between the semiquantitative volume percentage of PADRE imaging and SUVR of amyloid PET (Rs = 0.55; P = 0.02) in all subjects. CONCLUSION: Amyloid-ß-enhancing PADRE imaging can be used to predict the SUVR of amyloid PET, especially in the cuneus and precuneus, and may have the potential to be used for diagnosing AD by detecting amyloid deposition.

Improved reproducibility of diffusion tensor image analysis along the perivascular space (DTI-ALPS) index: an analysis of reorientation technique of the OASIS-3 dataset.

PURPOSE: Diffusion tensor image analysis along the perivascular space (DTI-ALPS) index is intended to reflect the glymphatic function of the brain; however, head rotation may reduce reproducibility and reliability. This study aimed to evaluate whether reorientation of DTI data improves the reproducibility of the ALPS index using the OASIS-3 dataset. MATERIALS AND METHODS: 234 cognitively normal subjects from the OASIS-3 dataset were included. Original and reoriented ALPS indices were calculated using a technique that registered vector information of DTI to another space and created reoriented diffusivity maps. The F test was used to compare variances of the original and reoriented ALPS indices. Subsequently, subjects with head rotation around the z- (inferior-superior; n = 43) or x axis (right-left; n = 25) and matched subjects with neutral head position were selected for evaluation of intra- and inter-rater reliability. Intraclass correlation coefficients (ICCs) of the original and reoriented ALPS indices for participants with head rotation and neutral head position were calculated separately. The Bland-Altman plot comparing the original and reoriented ALPS indices was also evaluated. RESULTS: The reoriented ALPS index exhibited a significantly smaller variance than the original ALPS index (p < 0.001). For intra- and inter-reliability, the reorientation technique showed good-to-excellent reproducibility in calculating the ALPS index even in subjects with head rotation (ICCs of original ALPS index: 0.52-0.81; ICCs of reoriented ALPS index: > 0.85). A wider range of the 95% limit of agreement of the Bland-Altman plot for subjects with x axis rotation was identified, indicating that x axis rotation may remarkably affect calculation of the ALPS index. CONCLUSION: The technique used in this study enabled the creation of reoriented diffusivity maps and improved reproducibility in calculating the ALPS index.

Clinical MRI morphological analysis of functional seizures compared to seizure-naïve and psychiatric controls.

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.

Visual and quantitative evaluation of microcalcifications in mammograms with deep learning-based super-resolution.

PURPOSE: To evaluate visually and quantitatively the performance of a deep-learning-based super-resolution (SR) model for microcalcifications in digital mammography. METHOD: Mammograms were consecutively collected from 5080 patients who underwent breast cancer screening from January 2015 to March 2017. Of these, 93 patients (136 breasts, mean age, 50 ± 7 years) had microcalcifications in their breasts on mammograms. We applied an artificial intelligence model known as a fast SR convolutional neural network to the mammograms. SR and original mammograms were visually evaluated by four breast radiologists using a 5-point scale (1: original mammograms are strongly preferred, 5: SR mammograms are strongly preferred) for the detection, diagnostic quality, contrast, sharpness, and noise of microcalcifications. Mammograms were quantitatively evaluated using a perception-based image-quality evaluator (PIQE). RESULTS: All radiologists rated the SR mammograms better than the original ones in terms of detection, diagnostic quality, contrast, and sharpness of microcalcifications. These ratings were significantly different according to the Wilcoxon signed-rank test (p <.001), while the noise score of the three radiologists was significantly lower (p <.001). According to PIQE, SR mammograms were rated better than the original mammograms, showing a significant difference by paired t-test (p <.001). CONCLUSION: An SR model based on deep learning can improve the visibility of microcalcifications in mammography and help detect and diagnose them in mammograms.

Incidence, molecular characteristics, and imaging features of "clinically-defined pseudoprogression" in newly diagnosed glioblastoma treated with chemoradiation.

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.

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.