Segmentation and Volume Estimation of the Habenula Using Deep Learning in Patients With Depression.

Background: The habenula is involved in the pathophysiology of depression. However, its small structure limits the accuracy of segmentation methods, and the findings regarding its volume have been inconsistent. This study aimed to create a highly accurate habenula segmentation model using deep learning, test its generalizability to clinical magnetic resonance imaging, and examine differences between healthy participants and patients with depression. Methods: This multicenter study included 382 participants (patients with depression: N = 234, women 47.0%; healthy participants: N = 148, women 37.8%). A 3-dimensional residual U-Net was used to create a habenula segmentation model on 3T magnetic resonance images. The reproducibility and generalizability of the predictive model were tested on various validation cohorts. Thereafter, differences between the habenula volume of healthy participants and that of patients with depression were examined. Results: A Dice coefficient of 86.6% was achieved in the derivation cohort. The test-retest dataset showed a mean absolute percentage error of 6.66, indicating sufficiently high reproducibility. A Dice coefficient of >80% was achieved for datasets with different imaging conditions, such as magnetic field strengths, spatial resolutions, and imaging sequences, by adjusting the threshold. A significant negative correlation with age was observed in the general population, and this correlation was more pronounced in patients with depression (p < 10-7, r = -0.59). Habenula volume decreased with depression severity in women even when the effects of age and scanner were excluded (p = .019, η2 = 0.099). Conclusions: Habenula volume could be a pathophysiologically relevant factor and diagnostic and therapeutic marker for depression, particularly in women.

Accurate segmentation of the habenula, a brain region implicated in depression, is challenging. In this study, we developed an automated human habenula segmentation model using deep learning techniques. The model was confirmed to be reproducible and generalizable at various spatial resolutions. Application of this model to a multicenter dataset confirmed that habenula volume decreased with age in healthy volunteers, an association that was more pronounced in individuals with depression. In addition, habenula volume decreased with the severity of depression in women. This novel model for habenula segmentation enables further study of the role of the habenula in depression.

Clinical and imaging characteristics of supratentorial glioma with IDH2 mutation.

PURPOSE: The rarity of IDH2 mutations in supratentorial gliomas has led to gaps in understanding their radiological characteristics, potentially resulting in misdiagnosis based solely on negative IDH1 immunohistochemical staining. We aimed to investigate the clinical and imaging characteristics of IDH2-mutant gliomas. METHODS: We analyzed imaging data from adult patients with pathologically confirmed diffuse lower-grade gliomas and known IDH1/2 alteration and 1p/19q codeletion statuses obtained from the records of our institute (January 2011 to August 2022, Cohort 1) and The Cancer Imaging Archive (TCIA, Cohort 2). Two radiologists evaluated clinical information and radiological findings using standardized methods. Furthermore, we compared the data for IDH2-mutant and IDH-wildtype gliomas. Multivariate logistic regression was used to identify the predictors of IDH2 mutation status, and receiver operating characteristic curve analysis was employed to assess the predictive performance of the model. RESULTS: Of the 20 IDH2-mutant supratentorial gliomas, 95% were in the frontal lobes, with 75% classified as oligodendrogliomas. Age and the T2-FLAIR discordance were independent predictors of IDH2 mutations. Receiver operating characteristic curve analysis for the model using age and T2-FLAIR discordance demonstrated a strong potential for discriminating between IDH2-mutant and IDH-wildtype gliomas, with an area under the curve of 0.96 (95% CI, 0.91-0.98, P = .02). CONCLUSION: A high frequency of oligodendrogliomas with 1p/19q codeletion was observed in IDH2-mutated gliomas. Younger age and the presence of the T2-FLAIR discordance were associated with IDH2 mutations and these findings may help with precise diagnoses and treatment decisions in clinical practice.

Severe Bronchopulmonary Dysplasia Adversely Affects Brain Growth in Preterm Infants.

INTRODUCTION: Bronchopulmonary dysplasia (BPD) is associated with neurodevelopmental outcomes of preterm infants, but its effect on brain growth in preterm infants after the neonatal period is unknown. This study aimed to evaluate the effect of severe BPD on brain growth of preterm infants from term to 18 months of corrected age (CA). METHODS: Sixty-three preterm infants (42 with severe BPD and 21 without severe BPD) who underwent magnetic resonance imaging at term equivalent age (TEA) and 18 months of CA were studied by using the Infant Brain Extraction and Analysis Toolbox (iBEAT). We measured segmented brain volumes and compared brain volume and brain growth velocity between the severe BPD group and the non-severe BPD group. RESULTS: There was no significant difference in brain volumes at TEA between the groups. However, the brain volumes of the total brain and cerebral white matter in the severe BPD group were significantly smaller than those in the non-severe BPD group at 18 months of CA. The brain growth velocities from TEA to 18 months of CA in the total brain, cerebral cortex, and cerebral white matter in the severe BPD group were lower than those in the non-severe BPD group. CONCLUSION: Brain growth in preterm infants with severe BPD from TEA age to 18 months of CA is less than that in preterm infants without severe BPD.

Quantitative susceptibility mapping and a nonlinearly transformed atlas for targeting the ventral intermediate nucleus of the thalamus in a patient with tremor and thalamic hypertrophy: illustrative case.

BACKGROUND: The ventral intermediate nucleus (Vim) of the thalamus is a surgical target for treating various types of tremor. Because it is difficult to visualize the Vim using standard magnetic resonance imaging, the structure is usually targeted based on the anterior and posterior commissures. This standard targeting method is practical in most patients but not in those with thalamic asymmetry. The authors examined the usefulness of quantitative susceptibility mapping (QSM) and transformed Vim atlas images to estimate the Vim localization in a patient with tremor and significant thalamic hypertrophy. OBSERVATIONS: A 51-year-old right-handed female had experienced a predominant left-hand action tremor for 6 years. Magnetic resonance imaging showed significant hypertrophy of the right thalamus and caudal shift of the thalamic ventral border. The authors referred to the QSM images to localize the decreased susceptibility area within the lateral ventral thalamic nuclei to target the Vim. In addition, the nonlinearly transformed Vim atlas images complemented the imaging-based targeting. The radiofrequency thalamotomy at the modified Vim target relieved the tremor completely. LESSONS: A combination of QSM and nonlinear transformation of the thalamic atlas can be helpful in the targeting method of the Vim for tremor patients with thalamic asymmetry.

The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI.

The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forward by enhancing image analysis and interpretation. The introduction of the Transformer architecture, followed by the development of Large Language Models (LLMs), has further revolutionized this domain. LLMs now possess the potential to automate and refine the radiology workflow, extending from report generation to assistance in diagnostics and patient care. The integration of multimodal technology with LLMs could potentially leapfrog these applications to unprecedented levels.However, LLMs come with unresolved challenges such as information hallucinations and biases, which can affect clinical reliability. Despite these issues, the legislative and guideline frameworks have yet to catch up with technological advancements. Radiologists must acquire a thorough understanding of these technologies to leverage LLMs' potential to the fullest while maintaining medical safety and ethics. This review aims to aid in that endeavor.

Thin-slice reverse encoding distortion correction DWI facilitates visualization of non-functioning pituitary neuroendocrine tumor (PitNET)/pituitary adenoma and surrounding normal structures.

BACKGROUND: To evaluate the clinical usefulness of thin-slice echo-planar imaging (EPI)-based diffusion-weighted imaging (DWI) with an on-console distortion correction technique, termed reverse encoding distortion correction DWI (RDC-DWI), in patients with non-functioning pituitary neuroendocrine tumor (PitNET)/pituitary adenoma. METHODS: Patients with non-functioning PitNET/pituitary adenoma who underwent 3-T RDC-DWI between December 2021 and September 2022 were retrospectively enrolled. Image quality was compared among RDC-DWI, DWI with correction for distortion induced by B0 inhomogeneity alone (B0-corrected-DWI), and original EPI-based DWI with anterior-posterior phase-encoding direction (AP-DWI). Susceptibility artifact, anatomical visualization of cranial nerves, overall tumor visualization, and visualization of cavernous sinus invasion were assessed qualitatively. Quantitative assessment of geometric distortion was performed by evaluation of anterior and posterior displacement between each DWI and the corresponding three-dimensional T2-weighted imaging. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient values were measured. RESULTS: Sixty-four patients (age 70.8 ± 9.9 years [mean ± standard deviation]; 33 females) with non-functioning PitNET/pituitary adenoma were evaluated. In terms of susceptibility artifacts in the frontal and temporal lobes, visualization of left trigeminal nerve, overall tumor visualization, and anterior displacement, RDC-DWI performed the best and B0-corrected-DWI performed better than AP-DWI. The right oculomotor and right trigeminal nerves were better visualized by RDC-DWI than by B0-corrected-DWI and AP-DWI. Visualization of cavernous sinus invasion and posterior displacement were better by RDC-DWI and B0-corrected-DWI than by AP-DWI. SNR and CNR were the highest for RDC-DWI. CONCLUSIONS: RDC-DWI achieved excellent image quality regarding susceptibility artifact, geometric distortion, and tumor visualization in patients with non-functioning PitNET/pituitary adenoma. RELEVANCE STATEMENT: RDC-DWI facilitates excellent visualization of the pituitary region and surrounding normal structures, and its on-console distortion correction technique is convenient. RDC-DWI can clearly depict cavernous sinus invasion of PitNET/pituitary adenoma even without contrast medium. KEY POINTS: • RDC-DWI is an EPI-based DWI technique with a novel on-console distortion correction technique. • RDC-DWI corrects distortion due to B0 field inhomogeneity and eddy current. • We evaluated the usefulness of thin-slice RDC-DWI in non-functioning PitNET/pituitary adenoma. • RDC-DWI exhibited excellent visualization in the pituitary region and surrounding structures. • In addition, the on-console distortion correction of RDC-DWI is clinically convenient.

Specific consistency score for rational selection of epilepsy resection surgery candidates.

OBJECTIVE: Degree of indication for epilepsy surgery is determined by taking multiple factors into account. This study aimed to investigate the usefulness of the Specific Consistency Score (SCS), a proposed score for focal epilepsy to rate the indication for epilepsy focal resection. METHODS: This retrospective cohort study included patients considered for resective epilepsy surgery in Kyoto University Hospital from 2011 to 2022. Plausible epileptic focus was tentatively defined. Cardinal findings were scored based on specificity and consistency with the estimated laterality and lobe. The total points represented SCS. The association between SCS and the following clinical parameters was assessed by univariate and multivariate analysis: (1) probability of undergoing resective epilepsy surgery, (2) good postoperative seizure outcome (Engel I and II or Engel I only), and (3) lobar concordance between the noninvasively estimated focus and intracranial electroencephalographic (EEG) recordings. RESULTS: A total of 131 patients were evaluated. Univariate analysis revealed higher SCS in the (1) epilepsy surgery group (8.4 [95% confidence interval (CI) = 7.8-8.9] vs. 4.9 [95% CI = 4.3-5.5] points; p < .001), (2) good postoperative seizure outcome group (Engel I and II; 8.7 [95% CI = 8.2-9.3] vs. 6.4 [95% CI = 4.5-8.3] points; p = .008), and (3) patients whose focus defined by intracranial EEG matched the noninvasively estimated focus (8.3 [95% CI = 7.3-9.2] vs. 5.4 [95% CI = 3.5-7.3] points; p = .004). Multivariate analysis revealed areas under the curve of .843, .825, and .881 for Parameters 1, 2, and 3, respectively. SIGNIFICANCE: SCS provides a reliable index of good indication for resective epilepsy surgery and can be easily available in many institutions not necessarily specializing in epilepsy.

Fairness of artificial intelligence in healthcare: review and recommendations.

In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.

Vessel wall MRI in moyamoya disease: arterial wall enhancement varies depending on age, arteries, and disease progression.

OBJECTIVE: To investigate the relationship of followings for patients with moyamoya disease (MMD): arterial wall enhancement on vessel wall MRI (VW-MRI), cross-sectional area (CSA), time-of-flight MR angiography (MRA), age, locations from intracranial internal carotid artery (ICA) to proximal middle cerebral artery (MCA), disease progression, and transient ischemic attack (TIA). METHODS: Patients who underwent VW-MRI between October 2018 and December 2020 were enrolled in this retrospective study. We measured arterial wall enhancement (enhancement ratio, ER) and CSA at five sections of ICA and MCA. Also, we scored MRA findings. Multiple linear regression (MLR) analysis was performed to explore the associations between ER, age, MRA score, CSA, history of TIA, and surgical revascularization. RESULTS: We investigated 102 sides of 51 patients with MMD (35 women, 16 men, mean age 31 years ± 18 [standard deviation]). ER for MRA score 2 (signal discontinuity) was higher than ER for other scores in sections D (end of ICA) and E (proximal MCA) on MLR analysis. ER in section E was significantly higher in patients for MRA score 2 with TIA history than without. ER significantly increased as CSA increased in section E, which suggests ER becomes less in decreased CSA due to negative remodeling. CONCLUSION: Arterial wall enhancement in MMD varies by age, location, and disease progression. Arterial wall enhancement may be stronger in the progressive stage of MMD. Arterial wall enhancement increases with history of TIA at proximal MCA, which may indicate the progression of the disease. CLINICAL RELEVANCE STATEMENT: Arterial wall enhancement in moyamoya disease varies by age, location of arteries, and disease progression, and arterial wall enhancement may be used as an imaging biomarker of moyamoya disease. KEY POINTS: It has not been clarified what arterial wall enhancement in moyamoya disease represents. Arterial wall enhancement in moyamoya disease varies by age, location of arteries, and disease progression. Arterial wall enhancement in moyamoya disease increases as the disease progresses.

One-year lung function decline in sarcoidosis.

BACKGROUND: The definition of progressive pulmonary fibrosis is based on a 1-year lung function decline. OBJECTIVES: To evaluate the epidemiology and clinical relevance of 1-year lung function decline in sarcoidosis. METHODS: A retrospective observational study at a general sarcoidosis clinic. RESULTS: Of the 198 patients, 42 (18.4 %) had a 1-year lung function decline (absolute 12-month decline in percentage predicted forced vital capacity [%FVC] of ≥5 % or percentage predicted diffusion capacity for carbon monoxide [%DLCO] of ≥10 %). A 1-year lung function decline was associated with a 2-year lung function decline (a relative 24-month decline in %FVC of ≥10 % or %DLCO of ≥15 %), which occurred in 13 (7.4 %) of the 175 patients with 24-month follow-up results. A 1-year lung function decline was not associated with survival; a 2-year lung function decline predicted mortality. CONCLUSIONS: Compared with a 24-month decline, a 12-month decline in lung function did not predict worse survival in sarcoidosis.

Revolutionizing radiation therapy: the role of AI in clinical practice.

This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist's perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.

Ruptured thrombosed vertebral artery dissecting aneurysm treated with staged flow diverter after prediction of the rupture point by vessel wall MRI.

The safety and feasibility of using staged flow diverter (FD) for ruptured cerebral aneurysms, in which coil embolization is performed in the acute phase and FD is deployed in the subacute phase, has recently been reported. This strategy requires assuming the rupture point and performing coil embolization. Although vessel wall magnetic resonance imaging (VW-MRI) has been reported to be useful in predicting the rupture point of aneurysms, its use with staged FD has not yet been reported. We report the first case of staged FD with preoperative contrast-enhanced VW-MRI to predict the rupture point for partially thrombosed vertebral artery dissecting large aneurysm involving posterior inferior cerebellar artery (PICA) origin. This approach achieved a very good outcome, not only completely occluding the aneurysm, but also reconstructing the parent artery while maintaining the patency of the PICA.

Evaluating Reproducibility of the ADC and Distortion in Diffusion-weighted Imaging (DWI) with Reverse Encoding Distortion Correction (RDC).

PURPOSE: To compare image distortion and reproducibility of quantitative values between reverse encoding distortion correction (RDC) diffusion-weighted imaging (DWI) and conventional DWI techniques in a phantom study and in healthy volunteers. METHODS: This prospective study was conducted with the approval of our institutional review board. Written informed consent was obtained from each participant. RDC-DWIs were created from images obtained at 3T in three orthogonal directions in a phantom and in 10 participants (mean age, 70.9 years; age range, 63-83 years). Images without distortion correction (noDC-DWI) and those corrected with B0 (B0c-DWI) were also created. To evaluate distortion, coefficients of variation were calculated for each voxel and ROIs were placed at four levels of the brain. To evaluate the reproducibility of apparent diffusion coefficient (ADC) measurements, intra- and inter-scan variability (%CVADC) were calculated from repeated scans of the phantom. Analysis was performed using Wilcoxon signed-rank test with Bonferroni correction, and P < 0.05 was considered statistically significant. RESULTS: In the phantom, distortion was less in RDC-DWI than in B0c-DWI (P < 0.006), and was less in B0c-DWI than in noDC-DWI (P < 0.006). Intra-scan %CVADC was within 1.30%, and inter-scan %CVADC was within 2.99%. In the volunteers, distortion was less in RDC-DWI than in B0c-DWI in three of four locations (P < 0.006), and was less in B0c-DWI than in noDC-DWI (P < 0.006). At the middle cerebellar peduncle, distortion was less in RDC-DWI than in noDC-DWI (P < 0.006), and was less in noDC-DWI than in B0c-DWI (P < 0.0177). CONCLUSION: In both the phantom and in volunteers, distortion was the least in RDC-DWI than in B0c-DWI and noDC-DWI.

"Transcallosal" periventricular anastomosis in moyamoya disease: the fourth periventricular anastomosis and a potential predictor of hemorrhage.

OBJECTIVES: Choroidal anastomosis is a risk factor for hemorrhage in moyamoya disease. One variant of choroidal anastomosis, "transcallosal anastomosis," originates from the medial posterior choroidal artery, and penetrates the corpus callosum to reconstruct the pericallosal artery. We aimed to investigate the prevalence and the bleeding rate of transcallosal anastomosis using sliding thin-slab maximum intensity projection reformatted from magnetic resonance angiography (MRA). MATERIALS AND METHODS: This study included 222 patients. We defined transcallosal anastomosis grades (0-2) and the stenosis of the anterior (ACA, 0-2), middle (MCA, 1-3), and posterior cerebral artery (PCA, 0-2) by MRA scores, independently by two coauthors. RESULTS: Grade-2 transcallosal anastomosis was detected in 21 patients (9.5 %). There were no correlations of the incidence of transcallosal anastomosis with previous bypass surgery (P = 0.23). Multivariate analysis revealed a significantly higher incidence in hemorrhagic onset and younger age (odds ratio [OR] 3.77, and 0.97). Transcallosal anastomosis had statistically significant correlation with ACA and PCA scores (P = 0.01 and 0.03), but not with MCA scores (P = 0.1). In multivariate analysis, ACA scores 1 and 2 were significantly higher (OR, 15.44 and 11.17), and PCA score 1 was also higher (OR, 3.07), but PCA score 2 was not. Interrater agreement for judgment of transcallosal anastomosis grade was strong (κ = 0.89). Two patients with Grade-2 transcallosal anastomosis had late hemorrhage in the corpus callosum (bleeding rate: 2.5 % per year). CONCLUSIONS: Transcallosal anastomosis may be associated with both advanced ACA and moderate PCA stenosis, and cause hemorrhage at the corpus callosum.

Partially thrombosed giant basilar artery aneurysm with attenuated contrast enhancement of the intraluminal thrombus on vessel wall MRI after flow diversion treatment: illustrative case.

BACKGROUND: The effect of vessel wall magnetic resonance imaging (VW-MRI) enhancement in partially thrombosed aneurysms has previously indicated aneurysmal instability and a rupture risk. However, whether the contrast effect of the wall changes before or after flow diversion treatment is still under investigation. OBSERVATIONS: The authors report a case of a partially thrombosed basilar artery aneurysm that increased in size over a short period, worsened brainstem compression symptoms, and was treated with a flow diverter stent with good results. In this case, VW-MRI after surgery showed a reduced contrast effect on the intraluminal thrombus within the aneurysm. The aneurysm thrombosed and markedly regressed over the next 5 months, with remarkable improvement in the brainstem compression symptoms. LESSONS: This finding on VW-MRI may indicate an attenuation of neovascularization in the thrombus wall and be a sign of aneurysm stabilization.

Value of Quantitative Susceptibility Mapping in Clinical Neuroradiology.

Quantitative susceptibility mapping (QSM) is a unique technique for providing quantitative information on tissue magnetic susceptibility using phase image data. QSM can provide valuable information regarding physiological and pathological processes such as iron deposition, hemorrhage, calcification, and myelin. QSM has been considered for use as an imaging biomarker to investigate physiological status and pathological changes. Although various studies have investigated the clinical applications of QSM, particularly regarding the use of QSM in clinical practice, have not been examined well. This review provides on an overview of the basics of QSM and its clinical applications in neuroradiology. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Incidence of neurocutaneous melanosis in Japanese pediatric patients with congenital melanocytic nevi.

Neurocutaneous melanosis (NCM) is a rare, non-hereditary neurocutaneous disorder characterized by excessive melanocytic proliferation in the skin and central nervous system. As no major studies have covered the incidence of NCM among Japanese patients with congenital melanocytic nevi (CMN), we prospectively investigated the incidence of NCM among Japanese patients who underwent initial treatment for CMN. The relationship of CMN and NCM was also investigated. Japanese pediatric patients with CMN under 1 year of age were included between January 2020 and November 2022, and all patients underwent brain MRI to check for NCM in this study. NCM lesions were most frequently seen in the amygdala, followed by the cerebellum, brainstem, and cerebral hemispheres. NCM was diagnosed on brain MRI in 31.6% of the 38 patients with CMN and in 25.0% of patients with no prior examination or treatment. Distribution and size of CMN, number of satellite nevi, rugosity and nodules were strongly associated with the existence of NCM, and these findings may guide a future registry study with a large cohort of CMN patients.

From FDG and beyond: the evolving potential of nuclear medicine.

The radiopharmaceutical 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG) has been dominantly used in positron emission tomography (PET) scans for over 20 years, and due to its vast utility its applications have expanded and are continuing to expand into oncology, neurology, cardiology, and infectious/inflammatory diseases. More recently, the addition of artificial intelligence (AI) has enhanced nuclear medicine diagnosis and imaging with FDG-PET, and new radiopharmaceuticals such as prostate-specific membrane antigen (PSMA) and fibroblast activation protein inhibitor (FAPI) have emerged. Nuclear medicine therapy using agents such as [177Lu]-dotatate surpasses conventional treatments in terms of efficacy and side effects. This article reviews recently established evidence of FDG and non-FDG drugs and anticipates the future trajectory of nuclear medicine.

New trend in artificial intelligence-based assistive technology for thoracic imaging.

Although there is no solid agreement for artificial intelligence (AI), it refers to a computer system with intelligence similar to that of humans. Deep learning appeared in 2006, and more than 10 years have passed since the third AI boom was triggered by improvements in computing power, algorithm development, and the use of big data. In recent years, the application and development of AI technology in the medical field have intensified internationally. There is no doubt that AI will be used in clinical practice to assist in diagnostic imaging in the future. In qualitative diagnosis, it is desirable to develop an explainable AI that at least represents the basis of the diagnostic process. However, it must be kept in mind that AI is a physician-assistant system, and the final decision should be made by the physician while understanding the limitations of AI. The aim of this article is to review the application of AI technology in diagnostic imaging from PubMed database while particularly focusing on diagnostic imaging in thorax such as lesion detection and qualitative diagnosis in order to help radiologists and clinicians to become more familiar with AI in thorax.