To develop and validate a novel nomogram combining multi-organ PET metabolic metrics for major pathological response (MPR) prediction in resectable non-small cell lung cancer (rNSCLC) patients receiving neoadjuvant immunochemotherapy. This retrospective cohort included rNSCLC patients who underwent baseline [¹⁸F]F-FDG PET/CT prior to neoadjuvant immunochemotherapy at Xiangya Hospital from April 2020 to April 2024. Patients were randomly stratified into training (70%) and validation (30%) cohorts. Using deep learning-based automated segmentation, we quantified metabolic parameters (SUV_mean, SUV_max, SUV_peak, MTV, TLG) and their ratio to liver metabolic parameters for primary tumors and nine key organs. Feature selection employed a tripartite approach: univariate analysis, LASSO regression, and random forest optimization. The final multivariable model was translated into a clinically interpretable nomogram, with validation assessing discrimination, calibration, and clinical utility. Among 115 patients (MPR rate: 63.5%, n = 73), five metabolic parameters emerged as predictive biomarkers for MPR: Spleen_SUV_mean, Colon_SUV_peak, Spine_TLG, Lesion_TLG, and Spleen-to-Liver SUV_max ratio. The nomogram demonstrated consistent performance across cohorts (training AUC = 0.78 [95%CI 0.67-0.88]; validation AUC = 0.78 [95%CI 0.62-0.94]), with robust calibration and enhanced clinical net benefit on decision curve analysis. Compared to tumor-only parameters, the multi-organ model showed higher specificity (100% vs. 92%) and positive predictive value (100% vs. 90%) in the validation set, maintaining 76% overall accuracy. This first-reported multi-organ metabolic nomogram noninvasively predicts MPR in rNSCLC patients receiving neoadjuvant immunochemotherapy, outperforming conventional tumor-centric approaches. By quantifying systemic host-tumor metabolic crosstalk, this tool could help guide personalized therapeutic decisions while mitigating treatment-related risks, representing a paradigm shift towards precision immuno-oncology management.

In medical images, lymph nodes (LNs) have fuzzy boundaries, diverse shapes and sizes, and structures similar to surrounding tissues. To automatically segment uterine LNs from sagittal magnetic resonance (MRI) scans, we combined T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) images and tested the final results in our proposed model. This study used a data set of 158 MRI images of patients with FIGO staged LN confirmed by pathology. To improve the robustness of the model, data augmentation was applied to expand the data set. The training data was manually annotated by two experienced radiologists. The DWI and T2 images were fused and inputted into U-Net. The efficient channel attention (ECA) module was added to U-Net. A residual network was added to the encoding-decoding stage, named Efficient residual U-Net (ERU-Net), to obtain the final segmentation results and calculate the mean intersection-over-union (mIoU). The experimental results demonstrated that the ERU-Net network showed strong segmentation performance, which was significantly better than other segmentation networks. The mIoU reached 0.83, and the average pixel accuracy was 0.91. In addition, the precision was 0.90, and the corresponding recall was 0.91. In this study, ERU-Net successfully achieved the segmentation of LN in uterine MRI images. Compared with other segmentation networks, our network has the best segmentation effect on uterine LN. This provides a valuable reference for doctors to develop more effective and efficient treatment plans.

The purpose of this study was to investigate the impact of optic nerve tortuosity (ONT), and the interaction of globe proptosis and size on retinal ganglion cell (RGC) thickness, using retinal nerve fiber layer (RNFL) thickness, across general, glaucoma, and myopic populations. This study analyzed 17,940 eyes from the UKBiobank cohort (ID 76442), including 72 glaucomatous and 2475 myopic eyes. Artificial intelligence models were developed to derive RNFL thickness corrected for ocular magnification from 3D optical coherence tomography scans and orbit features from 3D magnetic resonance images, including ONT, globe proptosis, axial length, and a novel feature: the interzygomatic line-to-posterior pole (ILPP) distance - a composite marker of globe proptosis and size. Generalized estimating equation (GEE) models evaluated associations between orbital and retinal features. RNFL thickness was positively correlated with ONT and ILPP distance (r = 0.065, P < 0.001 and r = 0.206, P < 0.001, respectively) in the general population. The same was true for glaucoma (r = 0.040, P = 0.74 and r = 0.224, P = 0.059), and for myopia (r = 0.069, P < 0.001 and r = 0.100, P < 0.001). GEE models revealed that straighter optic nerves and shorter ILPP distance were predictive of thinner RNFL in all populations. Straighter optic nerves and decreased ILPP distance could cause RNFL thinning, possibly due to greater traction forces. ILPP distance emerged as a potential biomarker of axonal health. These findings underscore the importance of orbit structures in RGC axonal health and warrant further research into orbit biomechanics.

We investigated pharmacokinetics, dosimetric patterns, and absorbed dose (AD)-effect correlations in [¹⁷⁷Lu]Lu-DOTATATE peptide receptor radionuclide therapy (PRRT) for metastatic neuroendocrine tumors (NETs) to develop strategies for future personalized dosimetry-guided treatments. <b>Methods:</b> Patients treated with standard [¹⁷⁷Lu]Lu-DOTATATE PRRT were recruited for serial SPECT/CT imaging. Kidneys were segmented on CT using a deep learning algorithm, and tumors were segmented at each cycle using a SPECT gradient-based tool, guided by radiologist-defined contours on baseline CT/MRI. Dosimetry was performed using an automated workflow that included contour intensity-based SPECT-SPECT registration, generation of Monte Carlo dose-rate maps, and dose-rate fitting. Lesion-level response at first follow-up was evaluated using both radiologic (RECIST and modified RECIST) and [⁶⁸Ga]Ga-DOTATATE PET-based criteria. Kidney toxicity was evaluated based on the estimated glomerular filtration rate (eGFR) at 9 mo after PRRT. <b>Results:</b> Dosimetry was performed after cycle 1 in 30 patients and after all cycles in 22 of 30 patients who completed SPECT/CT imaging after each cycle. Median cumulative tumor (<i>n</i> = 78) AD was 2.2 Gy/GBq (range, 0.1-20.8 Gy/GBq), whereas median kidney AD was 0.44 Gy/GBq (range, 0.25-0.96 Gy/GBq). The tumor-to-kidney AD ratio decreased with each cycle (median, 6.4, 5.7, 4.7, and 3.9 for cycles 1-4) because of a decrease in tumor AD, while kidney AD remained relatively constant. Higher-grade (grade 2) and pancreatic NETs showed a significantly larger drop in AD with each cycle, as well as significantly lower AD and effective half-life (T_eff), than did low-grade (grade 1) and small intestinal NETs, respectively. T_eff remained relatively constant with each cycle for both tumors and kidneys. Kidney T_eff and AD were significantly higher in patients with low eGFR than in those with high eGFR. Tumor AD was not significantly associated with response measures. There was no nephrotoxicity higher than grade 2; however, a significant negative association was found in univariate analyses between eGFR at 9 mo and AD to the kidney, which improved in a multivariable model that also adjusted for baseline eGFR (cycle 1 AD, <i>P</i> = 0.020, adjusted <i>R</i> ² = 0.57; cumulative AD, <i>P</i> = 0.049, adjusted <i>R</i> ² = 0.65). The association between percentage change in eGFR and AD to the kidney was also significant in univariate analysis and after adjusting for baseline eGFR (cycle 1 AD, <i>P</i> = 0.006, adjusted <i>R</i> ² = 0.21; cumulative AD, <i>P</i> = 0.019, adjusted <i>R</i> ² = 0.21). <b>Conclusion:</b> The dosimetric behavior we report over different cycles and for different NET subgroups can be considered when optimizing PRRT to individual patients. The models we present for the relationship between eGFR and AD have potential for clinical use in predicting renal function early in the treatment course. Furthermore, reported pharmacokinetics for patient subgroups allow more appropriate selection of population parameters to be used in protocols with fewer imaging time points that facilitate more widespread adoption of dosimetry.

Multiparametric Magnetic Resonance Imaging (mpMRI) is the gold standard for diagnosing brain tumors, especially gliomas, which are difficult to segment due to their heterogeneity and varied sub-regions. While manual segmentation is time-consuming and error-prone, Deep Learning (DL) automates the process with greater accuracy and speed. We conducted ablation studies on surveyed articles to evaluate the impact of "add-on" modules-addressing challenges like spatial information loss, class imbalance, and overfitting-on glioma segmentation performance. Advanced modules-such as atrous (dilated) convolutions, inception, attention, transformer, and hybrid modules-significantly enhance segmentation accuracy, efficiency, multiscale feature extraction, and boundary delineation, while lightweight modules reduce computational complexity. Experiments on the Brain Tumor Segmentation (BraTS) dataset (comprising low- and high-grade gliomas) confirm their robustness, with top-performing models achieving high Dice score for tumor sub-regions. This survey underscores the need for optimal module selection and placement to balance speed, accuracy, and interpretability in glioma segmentation. Future work should focus on improving model interpretability, lowering computational costs, and boosting generalizability. Tools like NeuroQuant® and Raidionics demonstrate potential for clinical translation. Further refinement could enable regulatory approval, advancing precision in brain tumor diagnosis and treatment planning.

This paper introduces an efficient sub-model ensemble framework aimed at enhancing the interpretability of medical deep learning models, thus increasing their clinical applicability. By generating uncertainty maps, this framework enables end-users to evaluate the reliability of model outputs. We developed a strategy to generate diverse models from a single well-trained checkpoint, facilitating the training of a model family. This involves producing multiple outputs from a single input, fusing them into a final output, and estimating uncertainty based on output disagreements. Implemented using U-Net and UNETR models for segmentation and synthesis tasks, this approach was tested on CT body segmentation and MR-CT synthesis datasets. It achieved a mean Dice coefficient of 0.814 in segmentation and a Mean Absolute Error of 88.17 HU in synthesis, improved from 89.43 HU by pruning. Additionally, the framework was evaluated under image corruption and data undersampling, maintaining correlation between uncertainty and error, which highlights its robustness. These results suggest that the proposed approach not only maintains the performance of well-trained models but also enhances interpretability through effective uncertainty estimation, applicable to both convolutional and transformer models in a range of imaging tasks.

Evaluating AI-generated medical image segmentations for clinical acceptability poses a significant challenge, as traditional pixelagreement metrics often fail to capture true diagnostic utility. This paper introduces Hierarchical Clinical Reasoner (HCR), a novel framework that leverages Large Language Models (LLMs) as clinical guardrails for reliable, zero-shot quality assessment. HCR employs a structured, multistage prompting strategy that guides LLMs through a detailed reasoning process, encompassing knowledge recall, visual feature analysis, anatomical inference, and clinical synthesis, to evaluate segmentations. We evaluated HCR on a diverse dataset across six medical imaging tasks. Our results show that HCR, utilizing models like Gemini 2.5 Flash, achieved a classification accuracy of 78.12%, performing comparably to, and in instances exceeding, dedicated vision models such as ResNet50 (72.92% accuracy) that were specifically trained for this task. The HCR framework not only provides accurate quality classifications but also generates interpretable, step-by-step reasoning for its assessments. This work demonstrates the potential of LLMs, when appropriately guided, to serve as sophisticated evaluators, offering a pathway towards more trustworthy and clinically-aligned quality control for AI in medical imaging.

Pericoronary adipose tissue (PCAT) is a key cardiovascular risk biomarker, yet its temporal changes after heart transplantation (HT) and comparison with controls remain unclear. This study investigates the temporal changes of PCAT in stable HT recipients and compares it to controls. In this study, we analyzed 159 stable HT recipients alongside two control groups. Both control groups were matched to a subgroup of HT recipients who did not have coronary artery stenosis. Group 1 consisted of 60 individuals matched for age, sex, and body mass index (BMI), with no history of hypertension, diabetes, hyperlipidemia, or smoking. Group 2 included 56 individuals additionally matched for hypertension, diabetes, hyperlipidemia, and smoking history. PCAT volume and fat attenuation index (FAI) were measured using AI-based software. Temporal changes in PCAT were assessed at multiple time points in HT recipients, and PCAT in the subgroup of HT recipients without coronary stenosis was compared to controls. Stable HT recipients exhibited a progressive decrease in FAI and an increase in PCAT volume over time, particularly in the first five years post-HT. Similar trends were observed in the subgroup of HT recipients without coronary stenosis. Compared to controls, PCAT FAI was significantly higher in the HT subgroup during the first five years post-HT (P < 0.001). After five years, differences persisted but diminished, with no statistically significant differences observed in the PCAT of left anterior descending artery (LAD) (P > 0.05). A negative correlation was observed between FAI and PCAT volume post-HT (r = - 0.75 ∼ - 0.53). PCAT volume and FAI undergo temporal changes in stable HT recipients, especially during the first five years post-HT. Even in HT recipients without coronary stenosis, PCAT FAI differs from controls, indicating distinct changes in this cohort.

The size of an individual's tongue relative to the oral cavity is associated with articulation speed [Feng, Lu, Zheng, Chi, and Honda, in Proceedings of the 10th Biennial Asia Pacific Conference on Speech, Language, and Hearing (2017), pp. 17-19)] and may affect speech clarity. This study introduces an ultrasound-based method for measuring relative tongue size, termed ultrasound-based relative tongue size (uRTS), as a cost-effective alternative to the magnetic resonance imaging (MRI) based method. Using deep learning to extract the tongue contour, uRTS was calculated from tongue and oropharyngeal cavity sizes in the midsagittal plane. Results from ten speakers showed a strong correlation between uRTS and MRI-based measurements (r = 0.87) and a negative correlation with tongue movement speed (r = -0.73), indicating uRTS is a useful index for assessing tongue size.

Cervical length (CL) measurement using transvaginal ultrasound is an effective screening tool to assess the risk of preterm birth. An adequate assessment of CL is crucial, however, manual sonographic CL measurement is highly operator-dependent and cumbersome. Therefore, a reliable and reproducible automatic method for CL measurement is in high demand to reduce inter-rater variability and improve workflow. Despite the increasing use of artificial intelligence techniques in ultrasound, applying deep learning (DL) to analyze ultrasound images of the cervix remains a challenge due to low signal-to-noise ratios and difficulties in capturing the cervical canal, which appears as a thin line and with extremely low contrast against the surrounding tissues. To address these challenges, we have developed CL-Net, a novel DL network that incorporates expert anatomical knowledge to identify the cervix, similar to the approach taken by clinicians. CL-Net captures anatomical features related to CL measurement, facilitating the identification of the cervical canal. It then identifies the cervical canal and automatically provides reproducible and reliable CL measurements. CL-Net achieved a success rate of 95.5% in recognizing the cervical canal, comparable to that of human experts (96.4%). Furthermore, the differences between the CL measurements of CL-Net and ground truth were considerably smaller than those made by non-experts and were comparable to those made by experts (median 1.36 mm, IQR 0.87-2.82 mm, range 0.06-6.95 mm for straight cervix; median 1.31 mm, IQR 0.61-2.65 mm, range 0.01-8.18 mm for curved one).