Fine-Tuned Large Language Model for Extracting Patients on Pretreatment for Lung Cancer from a Picture Archiving and Communication System Based on Radiological Reports.

This study aimed to investigate the performance of a fine-tuned large language model (LLM) in extracting patients on pretreatment for lung cancer from picture archiving and communication systems (PACS) and comparing it with that of radiologists. Patients whose radiological reports contained the term lung cancer (3111 for training, 124 for validation, and 288 for test) were included in this retrospective study. Based on clinical indication and diagnosis sections of the radiological report (used as input data), they were classified into four groups (used as reference data): group 0 (no lung cancer), group 1 (pretreatment lung cancer present), group 2 (after treatment for lung cancer), and group 3 (planning radiation therapy). Using the training and validation datasets, fine-tuning of the pretrained LLM was conducted ten times. Due to group imbalance, group 2 data were undersampled in the training. The performance of the best-performing model in the validation dataset was assessed in the independent test dataset. For testing purposes, two other radiologists (readers 1 and 2) were also involved in classifying radiological reports. The overall accuracy of the fine-tuned LLM, reader 1, and reader 2 was 0.983, 0.969, and 0.969, respectively. The sensitivity for differentiating group 0/1/2/3 by LLM, reader 1, and reader 2 was 1.000/0.948/0.991/1.000, 0.750/0.879/0.996/1.000, and 1.000/0.931/0.978/1.000, respectively. The time required for classification by LLM, reader 1, and reader 2 was 46s/2539s/1538s, respectively. Fine-tuned LLM effectively extracted patients on pretreatment for lung cancer from PACS with comparable performance to radiologists in a shorter time.

Axillary Lymphadenopathy after COVID-19 Vaccination: Follow-up for Enlarged Lymph Nodes on MR Imaging.

PURPOSE: The purpose of this study was to investigate the longitudinal MRI characteristic of COVID-19-vaccination-related axillary lymphadenopathy by evaluating the size, T2-weighted signal intensity, and apparent diffusion coefficient (ADC) values. METHODS: COVID-19-vaccination-related axillary lymphadenopathy was observed in 90 of 433 health screening program participants on the chest region of whole-body axial MRIs in 2021, as reported in our previous study. Follow-up MRI was performed at an interval of approximately 1 year after the second vaccination dose from 2022 to 2023. The diameter, signal intensity on T2-weighted images, and ADC of the largest enlarged lymph nodes were measured on chest MRI. The values were compared between the post-vaccination MRI and the follow-up MRI, and statistically analyzed. RESULTS: Out of the 90 participants who had enlarged lymph nodes of 5 mm or larger in short axis after the second vaccination dose, 76 participants (45 men and 31 women, mean age: 61 years) were enrolled in the present study. The median short- and long-axis diameter of the enlarged lymph nodes was 7 mm and 9 mm for post-vaccination MRI and 4 mm and 6 mm for follow-up MRI, respectively. The median signal intensity relative to the muscle on T2-weighted images decreased (5.1 for the initial post-vaccination MRI and 3.6 for the follow-up MRI, P < .0001). The ADC values did not show a notable change and remained in a normal range. CONCLUSION: The enlarged axillary lymph nodes decreased both in size and in signal intensity on T2-weighted images of follow-up MRI. The ADC remained unchanged. Our findings may provide important information to establish evidence-based guidelines for conducting proper assessment and management of post-vaccination lymphadenopathy.

Multimodal imaging findings of intrahepatic cholangiocarcinoma arising from a biliary adenofibroma: a case report with radiological-pathological correlation.

PURPOSE: Biliary adenofibroma is a solid microcystic epithelial neoplasm in the liver, comprising microcystic and tubuloacinar glandular tissues lined by a non-mucin secreting biliary epithelium and supported by a fibrous stroma. It is an extremely rare benign tumor with potential for malignant transformation. Herein, we report the case of a 64-year-old woman diagnosed with intrahepatic cholangiocarcinoma arising from biliary adenofibroma. METHODS: Imaging studies revealed a tumor of 50 mm diameter, consisting of two components in S1 of the liver. The ventral portion of the tumor showed an ill-defined mass with early peripheral and gradual centripetal enhancement invading to the middle hepatic vein on computed tomography (CT), diffusion restriction on magnetic resonance images, and high fluorine-18-2-deoxy-D-glucose (FDG) uptake on positron emission tomography, like conventional intrahepatic cholangiocarcinoma. The dorsal portion showed a well-defined and low-attenuated mass with heterogeneous early enhancement and partial wash-out on CT, marked hyperintensity on heavily T2-weighted images, and low FDG uptake. The patient subsequently underwent extended left hepatectomy. RESULTS: Pathologically, the former was diagnosed as cholangiocarcinoma and the latter as biliary adenofibroma. We discuss the radiological-pathological correlation of the tumor with a literature review. CONCLUSION: Preoperative diagnosis of biliary adenofibroma is extremely challenging; however, clinically, it is crucial not to miss the presence of malignant findings.

Improving detection performance of hepatocellular carcinoma and interobserver agreement for liver imaging reporting and data system on CT using deep learning reconstruction.

PURPOSE: This study aimed to compare the hepatocellular carcinoma (HCC) detection performance, interobserver agreement for Liver Imaging Reporting and Data System (LI-RADS) categories, and image quality between deep learning reconstruction (DLR) and conventional hybrid iterative reconstruction (Hybrid IR) in CT. METHODS: This retrospective study included patients who underwent abdominal dynamic contrast-enhanced CT between October 2021 and March 2022. Arterial, portal, and delayed phase images were reconstructed using DLR and Hybrid IR. Two blinded readers independently read the image sets with detecting HCCs, scoring LI-RADS, and evaluating image quality. RESULTS: A total of 26 patients with HCC (mean age, 73 years ± 12.3) and 23 patients without HCC (mean age, 66 years ± 14.7) were included. The figures of merit (FOM) for the jackknife alternative free-response receiver operating characteristic analysis in detecting HCC averaged for the readers were 0.925 (reader 1, 0.937; reader 2, 0.913) in DLR and 0.878 (reader 1, 0.904; reader 2, 0.851) in Hybrid IR, and the FOM in DLR were significantly higher than that in Hybrid IR (p = 0.038). The interobserver agreement (Cohen's weighted kappa statistics) for LI-RADS categories was moderate for DLR (0.595; 95% CI, 0.585-0.605) and significantly superior to Hybrid IR (0.568; 95% CI, 0.553-0.582). According to both readers, DLR was significantly superior to Hybrid IR in terms of image quality (p ≤ 0.021). CONCLUSION: DLR improved HCC detection, interobserver agreement for LI-RADS categories, and image quality in evaluations of HCC compared to Hybrid IR in abdominal dynamic contrast-enhanced CT.

The Usefulness of Computer-Aided Detection of Brain Metastases on Contrast-Enhanced Computed Tomography Using Single-Shot Multibox Detector: Observer Performance Study.

OBJECTIVE: This study aimed to test the usefulness of computer-aided detection (CAD) for the detection of brain metastasis (BM) on contrast-enhanced computed tomography. METHODS: The test data set included whole-brain axial contrast-enhanced computed tomography images of 25 cases with 62 BMs and 5 cases without BM. Six radiologists from 3 institutions with 2 to 4 years of experience independently reviewed the cases, both in conditions with and without CAD assistance. Sensitivity, positive predictive value, number of false positives, and reading time were compared between the conditions using paired t tests. Subanalysis was also performed for groups of lesions divided according to size. A P value <0.05 was considered statistically significant. RESULTS: With CAD, sensitivity significantly increased from 80.4% to 83.9% ( P = 0.04), whereas positive predictive value significantly decreased from 88.7% to 84.8% ( P = 0.03). Reading time with and without CAD was 112 and 107 seconds, respectively ( P = 0.38), and the number of false positives was 10.5 with CAD and 7.0 without CAD ( P = 0.053). Sensitivity significantly improved for 6- to 12-mm lesions, from 71.2% without CAD to 80.3% with CAD ( P = 0.02). The sensitivity of the CAD (95.2%) was significantly higher than that of any reader (with CAD: P = 0.01; without CAD: P = 0.005). CONCLUSIONS: Computer-aided detection significantly improved BM detection sensitivity without prolonging reading time while marginally increased the false positives.

The effect of a post-scan processing denoising system on image quality and morphometric analysis.

PURPOSE: MR image quality and subsequent brain morphometric analysis are inevitably affected by noise. The purpose of this study was to evaluate the effectiveness of an artificial intelligence (AI)-based post-scan processing denoising system, intelligent Quick Magnetic Resonance (iQMR), on MR image quality and brain morphometric analysis. METHODS: We used 1.5T MP-RAGE MR images acquired from the Alzheimer's Disease Neuroimaging Initiative 1 database. The images of 21 subjects were used for cross-sectional analysis and 15 for longitudinal analysis. In the longitudinal analysis, two timepoints over a 2-year interval were used. Each subject was scanned twice at each timepoint. MR images processed with and without the denoising system were compared both visually and objectively using FreeSurfer cortical thickness analysis. RESULTS: The denoising system reduced the noise with good white-gray matter contrast (noise: p < 0.001; contrast: p = 0.49). The mean intraclass correlation coefficients (ICCs) of cortical thickness were slightly better in the images processed with the denoising system (0.739/0.859/0.883; Gaussian smoothing kernel of full width at half maximum = 0/10/20) compared with the unprocessed images (0.718/0.854/0.880). In the longitudinal analysis, the mean ICCs of symmetrized percent change improved in images processed with the denoising system (0.202/0.349/0.431) compared with the unprocessed images (0.167/0.325/0.404). In addition, the detectability of significant cortical thickness atrophy improved with denoising. CONCLUSION: We confirm that the AI-based denoising system could effectively reduce the noise while retaining the contrast. We also confirm the improvement of the reliability and detectability of brain morphometric analysis with the denoising system.

Physiological 2-deoxy-2-[fluorine-18] fluoro-D-glucose accumulation in the thenar eminence in adults: a single-center retrospective study.

OBJECTIVE: This study evaluated the frequency, and effect of physiological 2-deoxy-2-[fluorine-18] fluoro-D-glucose (FDG) tracer injection and its association with the penetration rates of mobile devices. METHODS: This retrospective analysis included 213 patients (mean age ± standard deviation, 66.2 ± 14.1 years; range 23-93 years; 125 men) who underwent FDG-positron emission tomography examination. Elevated FDG activity in the thenar eminence with maximum standardized uptake value (SUVmax) ≥ 2.5 was considered positive. Differences according to age, sex, laterality, and tracer injection side were evaluated using Fisher's exact test. Associations were assessed using Pearson's correlation coefficient. RESULTS: Twenty-three percent (49/213) of the patients had elevated FDG activity in the thenar eminence (mean SUVmax, 3.50 ± 1.04; range 2.5-6.3), including 18 with bilateral findings. No significant difference existed according to age (< 50 years vs. 50-69 years vs. ≥ 70 years), sex, laterality, or tracer injection side. No significant correlation existed between penetration rates of mobile devices and the findings (p = 0.08). CONCLUSION: Elevated FDG activity in the thenar eminence occurs in adults, regardless of age, sex, laterality, or tracer injection side. This should be considered a common physiological change that does not warrant any further investigation.

Delayed cerebral enhancement on post-mortem computed tomography due to residual contrast medium administered shortly before death.

Postmortem computed tomography (CT) is currently a well-known procedure and helps in postmortem investigations. In this case report, we report a unique postmortem CT finding: delayed cerebral enhancement associated with the antemortem infusion of contrast medium. A 72-year-old female lost consciousness at a restaurant and was taken to a hospital in an ambulance. Despite resuscitation efforts, she died of hypoxic-ischemic encephalopathy caused by cardiac arrest. About 6 h before her death, she underwent enhanced antemortem CT of the head. No abnormal enhancement was observed in the cerebral parenchyma. Then, 11 h after her death, she underwent unenhanced postmortem CT, which showed bilateral hyperdense caudate nucleus and putamina, due to residual iodinated contrast medium, in addition to other characteristic findings of hypoxic-ischemic encephalopathy. The mechanism underlying this phenomenon could be the destruction of the blood-brain barrier, and/or selective vulnerability, due to hypoxic-ischemic changes in the gray matter. Enhancement of basal ganglia on postmortem CT due to antemortem infusion of iodinated contrast medium might suggest hypoxic-ischemic encephalopathy, which should be noted in postmortem CT interpretations.