Individual prognosis assessment is of paramount importance for treatment decision-making and active surveillance in cancer patients. We aimed to propose a radiomic model based on pre- and post-therapy MRI features for predicting disease-free survival (DFS) in locally advanced rectal cancer (LARC) following neoadjuvant chemoradiotherapy (nCRT) and subsequent surgical resection. This retrospective study included a total of 126 LARC patients, which were randomly assigned to a training set (n = 84) and a validation set (n = 42). All patients underwent pre- and post-nCRT MRI scans. Radiomic features were extracted from higher resolution T2-weighted images. Pearson correlation analysis and ANOVA or Relief were utilized for identifying radiomic features associated with DFS. Pre-treatment, post-treatment, and delta radscores were constructed by machine learning algorithms. An individualized nomogram was developed based on significant radscores and clinical variables using multivariate Cox regression analysis. Predictive performance was evaluated by the C-index, calibration curve, and decision curve analysis. The results demonstrated that in the validation set, the clinical model including pre-surgery carcinoembryonic antigen (CEA), chemotherapy after radiotherapy, and pathological stage yielded a C-index of 0.755 (95% confidence interval [CI]: 0.739-0.771). While the optimal pre-, post-, and delta-radscores achieved C-indices of 0.724 (95%CI: 0.701-0.747), 0.701 (95%CI: 0.671-0.731), and 0.625 (95%CI: 0.589-0.661), respectively. The nomogram integrating pre-surgery CEA, pathological stage, alongside pre- and post-nCRT radscore, obtained the highest C-index of 0.833 (95%CI: 0.815-0.851). The calibration curve and decision curves exhibited good calibration and clinical usefulness of the nomogram. Furthermore, the nomogram categorized patients into high- and low-risk groups exhibiting distinct DFS (both P < 0.0001). The nomogram incorporating pre- and post-therapy radscores and clinical factors could predict DFS in patients with LARC, which helps clinicians in optimizing decision-making and surveillance in real-world settings.

Large language models (LLMs) and multi-modal large language models (MLLMs) represent the cutting-edge in artificial intelligence. This review provides a comprehensive overview of their capabilities and potential impact on radiology. Unlike most existing literature reviews focusing solely on LLMs, this work examines both LLMs and MLLMs, highlighting their potential to support radiology workflows such as report generation, image interpretation, EHR summarization, differential diagnosis generation, and patient education. By streamlining these tasks, LLMs and MLLMs could reduce radiologist workload, improve diagnostic accuracy, support interdisciplinary collaboration, and ultimately enhance patient care. We also discuss key limitations, such as the limited capacity of current MLLMs to interpret 3D medical images and to integrate information from both image and text data, as well as the lack of effective evaluation methods. Ongoing efforts to address these challenges are introduced.

Major depressive disorder (MDD) is a debilitating condition that is associated with changes in the default-mode network (DMN). Commonly reported features include alterations in gray matter volume (GMV), cortical thickness (CoT), and gyrification. A comprehensive examination of these variables using ultra-high field strength MRI and machine learning methods may lead to novel insights into the pathophysiology of depression and help develop a more personalized therapy. Cerebral images were obtained from 41 patients with confirmed MDD and 41 healthy controls, matched for age and gender, using a 7-T-MRI. DMN parcellation followed the Schaefer 600 Atlas. Based on the results of a mixed-model repeated measures analysis, a support vector machine (SVM) calculation followed by leave-one-out cross-validation determined the predictive ability of structural features for the presence of MDD. A consecutive permutation procedure identified which areas contributed to the classification results. Correlating changes in those areas with BDI-II and AMDP scores added an explanatory aspect to this study. CoT did not delineate relevant changes in the mixed model and was excluded from further analysis. The SVM achieved a good prediction accuracy of 0.76 using gyrification data. GMV was not a viable predictor for disease presence, however, it correlated in the left parahippocampal gyrus with disease severity as measured by the BDI-II. Structural data of the DMN may therefore contain the necessary information to predict the presence of MDD. However, there may be inherent challenges with predicting disease course or treatment response due to high GMV variance and the static character of gyrification. Further improvements in data acquisition and analysis may help to overcome these difficulties.

To determine an optimal region of interest (ROI) for opportunistic screening of osteoporosis in terms of short-term in vivo diagnostic precision. We included patients who underwent two CT scans and one dual-energy X-ray absorptiometry scan within a month in 2022. Deep-learning software automatically measured the attenuation in L1 using 54 ROIs (three slice thicknesses × six shapes × three intravertebral levels). To identify factors associated with a lower attenuation difference between the two CT scans, mixed-effect model analysis was performed with ROI-level (slice thickness, shape, intravertebral levels) and patient-level (age, sex, patient diameter, change in CT machine) factors. The root-mean-square standard deviation (RMSSD) and area under the receiver-operating-characteristic curve (AUROC) were calculated. In total, 73 consecutive patients (mean age ± standard deviation, 69 ± 9 years, 38 women) were included. A lower attenuation difference was observed in ROIs in images with slice thicknesses of 1 and 3 mm than that in images with a slice thickness of 5 mm (p < .001), in large elliptical ROIs (p = .007 or < .001, respectively), and in mid- or cranial-level ROIs than that in caudal-level ROIs (p < .001). No patient-level factors were significantly associated with the attenuation difference. Large, elliptical ROIs placed at the mid-level of L1 on images with 1- or 3-mm slice thicknesses yielded RMSSDs of 12.4-12.5 HU and AUROCs of 0.90. The largest possible regions of interest drawn in the mid-level trabecular portion of the L1 vertebra on thin-slice images may yield improvements in the precision of opportunistic screening for osteoporosis via CT.

The purpose of this study is to estimate the extent to which the implementation of deep learning reconstruction (DLR) may reduce the risk of radiation-induced cancer from CT examinations, utilizing real-world clinical data. We retrospectively analyzed scan data of adult patients who underwent body CT during two periods relative to DLR implementation at our facility: a 12-month pre-DLR phase (n = 5553) using hybrid iterative reconstruction and a 12-month post-DLR phase (n = 5494) with routine CT reconstruction transitioning to DLR. To ensure comparability between two groups, we employed propensity score matching 1:1 based on age, sex, and body mass index. Dose data were collected to estimate organ-specific equivalent doses and total effective doses. We assessed the average dose reduction post-DLR implementation and estimated the Lifetime Attributable Risk (LAR) for cancer per CT exam pre- and post-DLR implementation. The number of radiation-induced cancers before and after the implementation of DLR was also estimated. After propensity score matching, 5247 cases from each group were included in the final analysis. Post-DLR, the total effective body CT dose significantly decreased to 15.5 ± 10.3 mSv from 28.1 ± 14.0 mSv pre-DLR (p < 0.001), a 45% reduction. This dose reduction significantly lowered the radiation-induced cancer risk, especially among younger women, with the estimated annual cancer incidence from 0.247% pre-DLR to 0.130% post-DLR. The implementation of DLR has the possibility to reduce radiation dose by 45% and the risk of radiation-induced cancer from 0.247 to 0.130% as compared with the iterative reconstruction. Question Can implementing deep learning reconstruction (DLR) in routine CT scans significantly reduce radiation dose and the risk of radiation-induced cancer compared to hybrid iterative reconstruction? Findings DLR reduced the total effective body CT dose by 45% (from 28.1 ± 14.0 mSv to 15.5 ± 10.3 mSv) and decreased estimated cancer incidence from 0.247 to 0.130%. Clinical relevance Adopting DLR in clinical practice substantially lowers radiation exposure and cancer risk from CT exams, enhancing patient safety, especially for younger women, and underscores the importance of advanced imaging techniques.

To develop habitat and deep learning (DL) models from multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) habitat images categorized using the K-means clustering algorithm. Additionally, we aim to assess the predictive value of identified regions for early evaluation of the responsiveness of hepatocellular carcinoma (HCC) patients to treatment with transarterial chemoembolization (TACE) plus molecular targeted therapies (MTT) and anti-PD-(L)1. A total of 102 patients with HCC from two institutions (A, n = 63 and B, n = 39) who received TACE plus systemic therapy were enrolled from September 2020 to January 2024. Multiple CE-MRI sequences were used to outline 3D volumes of interest (VOI) of the lesion. Subsequently, K-means clustering was applied to categorize intratumoral voxels into three distinct subgroups, based on signal intensity values of images. Using data from institution A, the habitat model was built with the ExtraTrees classifier after extracting radiomics features from intratumoral habitats. Similarly, the Crossformer model and ResNet50 model were trained on multi-channel data in institution A, and a DL model with Transformer-based aggregation was constructed to predict the response. Finally, all models underwent validation at institution B. The Crossformer model and the habitat model both showed high area under the receiver operating characteristic curves (AUCs) of 0.869 and 0.877 (training cohort). In validation, AUC was 0.762 for the Crossformer model and 0.721 for the habitat model. The habitat model and DL model based on CE-MRI possesses the capability to non-invasively predict the efficacy of TACE plus systemic therapy in HCC patients, which is critical for precision treatment and patient outcomes.

To evaluate image quality and lesion conspicuity of zero echo time (ZTE) MRI reconstructed with deep learning (DL)-based algorithm versus conventional reconstruction and to assess DL ZTE performance against CT for bone loss measurements in shoulder instability. Forty-four patients (9 females; 33.5 ± 15.65 years) with symptomatic anterior glenohumeral instability and no previous shoulder surgery underwent ZTE MRI and CT on the same day. ZTE images were reconstructed with conventional and DL methods and post-processed for CT-like contrast. Two musculoskeletal radiologists, blinded to the reconstruction method, independently evaluated 20 randomized MR ZTE datasets with and without DL-enhancement for perceived signal-to-noise ratio, resolution, and lesion conspicuity at humerus and glenoid using a 4-point Likert scale. Inter-reader reliability was assessed using weighted Cohen's kappa (K). An ordinal logistic regression model analyzed Likert scores, with the reconstruction method (DL-enhanced vs. conventional) as the predictor. Glenoid track (GT) and Hill-Sachs interval (HSI) measurements were performed by another radiologist on both DL ZTE and CT datasets. Intermodal agreement was assessed through intraclass correlation coefficients (ICCs) and Bland-Altman analysis. DL ZTE MR bone images scored higher than conventional ZTE across all items, with significantly improved perceived resolution (odds ratio (OR) = 7.67, p = 0.01) and glenoid lesion conspicuity (OR = 25.12, p = 0.01), with substantial inter-rater agreement (K = 0.61 (0.38-0.83) to 0.77 (0.58-0.95)). Inter-modality assessment showed almost perfect agreement between DL ZTE MR and CT for all bone measurements (overall ICC = 0.99 (0.97-0.99)), with mean differences of 0.08 (- 0.80 to 0.96) mm for GT and - 0.07 (- 1.24 to 1.10) mm for HSI. DL-based reconstruction enhances ZTE MRI quality for glenohumeral assessment, offering osseous evaluation and quantification equivalent to gold-standard CT, potentially simplifying preoperative workflow, and reducing CT radiation exposure.

Recent advancements in neuroimaging and machine learning have significantly improved our ability to diagnose and categorize isocitrate dehydrogenase (IDH)-wildtype glioblastoma, a disease characterized by notable tumoral heterogeneity, which is crucial for effective treatment. Neuroimaging techniques, such as diffusion tensor imaging and magnetic resonance radiomics, provide noninvasive insights into tumor infiltration patterns and metabolic profiles, aiding in accurate diagnosis and prognostication. Machine learning algorithms further enhance glioblastoma characterization by identifying distinct imaging patterns and features, facilitating precise diagnoses and treatment planning. Integration of these technologies allows for the development of image-based biomarkers, potentially reducing the need for invasive biopsy procedures and enabling personalized therapy targeting specific pro-tumoral signaling pathways and resistance mechanisms. Although significant progress has been made, ongoing innovation is essential to address remaining challenges and further improve these methodologies. Future directions should focus on refining machine learning models, integrating emerging imaging techniques, and elucidating the complex interplay between imaging features and underlying molecular processes. This review highlights the pivotal role of neuroimaging and machine learning in glioblastoma research, offering invaluable noninvasive tools for diagnosis, prognosis prediction, and treatment planning, ultimately improving patient outcomes. These advances in the field promise to usher in a new era in the understanding and classification of IDH-wildtype glioblastoma.

Prostate-specific membrane antigen (PSMA) molecular imaging is widely used for disease assessment in prostate cancer (PC). Artificial intelligence (AI) platforms such as automated Prostate Cancer Molecular Imaging Standardized Evaluation (aPROMISE) identify and quantify locoregional and distant disease, thereby expediting lesion identification and standardizing reporting. Our aim was to evaluate the ability of the updated aPROMISE platform to assess treatment responses based on integration of the RECIP (Response Evaluation Criteria in PSMA positron emission tomography-computed tomography [PET/CT]) 1.0 classification. The study included 33 patients with castration-sensitive PC (CSPC) and 34 with castration-resistant PC (CRPC) who underwent PSMA-targeted molecular imaging before and ≥2 mo after completion of treatment. Tracer-avid lesions were identified using aPROMISE for pretreatment and post-treatment PET/CT scans. Detected lesions were manually approved by an experienced nuclear medicine physician, and total tumor volume (TTV) was calculated. Response was assessed according to RECIP 1.0 as CR (complete response), PR (partial response), PD (progressive disease), or SD (stable disease). KEY FINDINGS AND LIMITATIONS: aPROMISE identified 1576 lesions on baseline scans and 1631 lesions on follow-up imaging, 618 (35%) of which were new. Of the 67 patients, aPROMISE classified four as CR, 16 as PR, 34 as SD, and 13 as PD; five cases were misclassified. The agreement between aPROMISE and clinician validation was 89.6% (κ = 0.79). aPROMISE may serve as a novel assessment tool for treatment response that integrates PSMA PET/CT results and RECIP imaging criteria. The precision and accuracy of this automated process should be validated in prospective clinical studies. We used an artificial intelligence (AI) tool to analyze scans for prostate cancer before and after treatment to see if we could track how cancer spots respond to treatment. We found that the AI approach was successful in tracking individual tumor changes, showing which tumors disappeared, and identifying new tumors in response to prostate cancer treatment.