Neoadjuvant immunochemotherapy (nICT) has demonstrated significant potential in improving pathological response rates and survival outcomes for patients with locally advanced esophageal squamous cell carcinoma (ESCC). However, substantial interindividual variability in therapeutic outcomes highlights the urgent need for more precise predictive tools to guide clinical decision-making. Traditional biomarkers remain limited in both predictive performance and clinical feasibility. In recent years, the application of artificial intelligence (AI) in medical imaging has expanded rapidly. By incorporating voxel-level feature maps, the combination of radiomics and deep learning enables the extraction of rich textural, morphological, and microstructural features, while autonomously learning high-level abstract representations from clinical CT images, thereby revealing biological heterogeneity that is often imperceptible to conventional assessments. Leveraging these high-dimensional representations, AI models can provide more accurate predictions of nICT response. Future advancements in foundation models, multimodal integration, and dynamic temporal modeling are expected to further enhance the generalizability and clinical applicability of AI. AI-powered medical imaging is poised to support all stages of perioperative management in ESCC, playing a pivotal role in high-risk patient identification, dynamic monitoring of therapeutic response, and individualized treatment adjustment, thereby comprehensively advancing precision nICT.

Tumor deposits (TDs) are an important prognostic factor in rectal cancer. However, integrated models combining clinical, habitat radiomics, and deep learning (DL) features for preoperative TDs detection remain unexplored. To investigate fusion models based on MRI for preoperative TDs identification and prognosis in rectal cancer. Retrospective. Surgically diagnosed rectal cancer patients (n = 635): training (n = 259) and internal validation (n = 112) from center 1; center 2 (n = 264) for external validation. 1.5/3T, T2-weighted image (T2WI) using fast spin echo sequence. Four models (clinical, habitat radiomics, DL, fusion) were developed for preoperative TDs diagnosis (184 TDs positive). T2WI was segmented using nnUNet, and habitat radiomics and DL features were extracted separately. Clinical parameters were analyzed independently. The fusion model integrated selected features from all three approaches through two-stage selection. Disease-free survival (DFS) analysis was used to assess the models' prognostic performance. Intraclass correlation coefficient (ICC), logistic regression, Mann-Whitney U tests, Chi-squared tests, LASSO, area under the curve (AUC), decision curve analysis (DCA), calibration curves, Kaplan-Meier analysis. The AUCs for the four models ranged from 0.778 to 0.930 in the training set. In the internal validation cohort, the AUCs of clinical, habitat radiomics, DL, and fusion models were 0.785 (95% CI 0.767-0.803), 0.827 (95% CI 0.809-0.845), 0.828 (95% CI 0.815-0.841), and 0.862 (95% CI 0.828-0.896), respectively. In the external validation cohort, the corresponding AUCs were 0.711 (95% CI 0.599-0.644), 0.817 (95% CI 0.801-0.833), 0.759 (95% CI 0.743-0.773), and 0.820 (95% CI 0.770-0.860), respectively. TDs-positive patients predicted by the fusion model had significantly poorer DFS (median: 30.7 months) than TDs-negative patients (median follow-up period: 39.9 months). A fusion model may identify TDs in rectal cancer and could allow to stratify DFS risk. 3.

PurposeTo develop and externally validate a multimodal AI model for detecting ischaemia complicating small-bowel obstruction (SBO). MethodsWe combined 3D CT data with routine laboratory markers (C-reactive protein, neutrophil count) and, optionally, radiology report text. From two centers, 1,350 CT examinations were curated; 771 confirmed SBO scans were used for model development with patient-level splits. Ischemia labels were defined by surgical confirmation within 24 hours of imaging. Models (MViT, ResNet-101, DaViT) were trained as unimodal and multimodal variants. External testing was used for 66 independent cases from a third center. Two radiologists (attending, resident) read the test set with and without AI assistance. Performance was assessed using AUC, sensitivity, specificity, and 95% bootstrap confidence intervals; predictions included a confidence score. ResultsThe image-plus-laboratory model performed best on external testing (AUC 0.69 [0.59-0.79], sensitivity 0.89 [0.76-1.00], and specificity 0.44 [0.35-0.54]). Adding report text improved internal validation but did not generalize externally; image+text and full multimodal variants did not exceed image+laboratory performance. Without AI, the attending outperformed the resident (AUC 0.745 [0.617-0.845] vs 0.706 [0.581-0.818]); with AI, both improved, attending 0.752 [0.637-0.853] and resident 0.752 [0.629-0.867], rising to 0.750 [0.631-0.839] and 0.773 [0.657-0.867] with confidence display; differences were not statistically significant. ConclusionA multimodal AI that combines CT images with routine laboratory markers outperforms single-modality approaches and boosts radiologist readers performance notably junior, supporting earlier, more consistent decisions within the first 24 hours. Key PointsA multimodal artificial intelligence (AI) model that combines CT images with laboratory markers detected ischemia in small-bowel obstruction with AUC 0.69 (95% CI 0.59-0.79) and sensitivity 0.89 (0.76-1.00) on external testing, outperforming single-modality models. Adding report text did not generalize across sites: the image+text model fell from AUC 0.82 (internal) to 0.53 (external), and adding text to image+biology left external AUC unchanged (0.69) with similar specificity (0.43-0.44). With AI assistance both junior and senior readers improved; the juniors AUC rose from 0.71 to 0.77, reaching senior-level performance. Summary StatementA multicentric AI model combining CT and routine laboratory data (CRP and neutrophilia) improved radiologists detection of ischemia in small-bowel obstruction. This tool supports earlier decision-making within the first 24 hours.

Maternal-fetal Ultrasound is the primary modality for monitoring fetal development, yet automated segmentation remains challenging due to the scarcity of high-quality annotations. To address this limitation, we propose a semi-supervised learning framework that leverages information divergence for robust fetal ultrasound segmentation. Our method employs a lightweight convolutional network (1.47M parameters) and a Transformer-based network, trained jointly with labelled data through standard supervision and with unlabelled data via cross-supervision. To encourage consistent and confident predictions, we introduce an information divergence loss that combines per-pixel Kullback-Leibler divergence and Mutual Information Gap, effectively reducing prediction disagreement between the two models. In addition, we apply mixup on unlabelled samples to further enhance robustness. Experiments on two fetal ultrasound datasets demonstrate that our approach consistently outperforms seven state-of-the-art semi-supervised methods. When only 5% of training data is labelled, our framework improves the Dice score by 2.39%, reduces the 95% Hausdorff distance by 14.90, and decreases the Average Surface Distance by 4.18. These results highlight the effectiveness of leveraging information divergence for annotation-efficient and robust medical image segmentation. Our code is publicly available on GitHub.

We aimed to develop and validate a radiomics-based machine learning nomogram using multiparametric magnetic resonance imaging to preoperatively predict substantial lymphovascular space invasion in patients with endometrial cancer. This retrospective dual-center study included patients with histologically confirmed endometrial cancer who underwent preoperative magnetic resonance imaging (MRI). The patients were divided into training and test sets. Radiomic features were extracted from multiparametric magnetic resonance imaging to generate radiomic scores using a support vector machine algorithm. Three predictive models were constructed: clinical (Model^C), radiomics-only (Model^R), and fusion (Model^N). The models' performances were evaluated by analyzing their receiver operating characteristic curves, and pairwise comparisons of the models' areas under the curves were conducted using DeLong's test and adjusted using the Bonferroni correction. Decision curve analysis with integrated discrimination improvement was used for net benefit comparison. This study enrolled 283 women (training set: n = 198; test set: n = 85). The lymphovascular space invasion groups (substantial and no/focal) had significantly different radiomic scores (P < 0.05). Model^N achieved an area under the curve of 0.818 (95% confidence interval: 0.757-0.869) and 0.746 (95% confidence interval: 0.640-0.835) for the training and test sets, respectively, demonstrating a good fit according to the Hosmer-Lemeshow test (P > 0.05). The DeLong test with Bonferroni correction indicated that Model^N demonstrated better diagnostic efficiency than Model^C in predicting substantial lymphovascular space invasion in the two sets (adjusted P < 0.05). In addition, decision curve analysis demonstrated a higher net benefit for Model^N, with integrated discrimination improvements of 0.043 and 0.732 (P < 0.01) in the training and test sets, respectively. The multiparametric magnetic resonance imaging-based radiomics machine learning nomogram showed moderate diagnostic performance for substantial lymphovascular space invasion in patients with endometrial cancer.

This study aimed to evaluate the effectiveness of artificial intelligence (AI) in diagnosing focal nodular hyperplasia (FNH) of the liver using magnetic resonance imaging (MRI) and compare its performance with that of radiologists. In the first phase of the study, the MRIs of 60 patients (30 patients with FNH and 30 patients with no lesions or lesions other than FNH) were processed using a segmentation program and introduced to an AI model. After the learning process, the MRIs of 42 different patients that the AI model had no experience with were introduced to the system. In addition, a radiology resident and a radiology specialist evaluated patients with the same MR sequences. The sensitivity and specificity values were obtained from all three reviews. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the AI model were found to be 0.769, 0.966, 0.909, and 0.903, respectively. The sensitivity and specificity values were higher than those of the radiology resident and lower than those of the radiology specialist. The results of the specialist versus the AI model revealed a good agreement level, with a kappa (κ) value of 0.777. For the diagnosis of FNH, the sensitivity, specificity, PPV, and NPV of the AI device were higher than those of the radiology resident and lower than those of the radiology specialist. With additional studies focused on different specific lesions of the liver, AI models are expected to be able to diagnose each liver lesion with high accuracy in the future. AI is studied to provide assisted or automated interpretation of radiological images with an accurate and reproducible imaging diagnosis.

<i>Background:</i> Neuroendocrine neoplasms (NENs) are a diverse group of malignancies in which somatostatin receptor expression can be crucial in guiding therapy. We aimed to evaluate the effectiveness of [^99mTc]Tc-EDDA/HYNIC-TOC SPECT/CT in differentiating neuroendocrine tumor histology, selecting candidates for radioligand therapy, and identifying correlations between somatostatin receptor expression and non-imaging parameters in metastatic NENs. <i>Methods:</i> This retrospective study included 65 patients (29 women, 36 men, mean age 61) with metastatic neuroendocrine neoplasms confirmed by histology, follow-up, or imaging, comprising 14 poorly differentiated carcinomas and 51 well-differentiated tumors. Somatostatin receptor SPECT/CT results were assessed visually and semiquantitatively, with mathematical models incorporating histological, oncological, immunohistochemical, and laboratory parameters, followed by biostatistical analysis. <i>Results:</i> Of 392 lesions evaluated, the majority were metastases in the liver, lymph nodes, and bones. Mathematical models estimated somatostatin receptor expression accurately (70-83%) based on clinical parameters alone. Key factors included tumor origin, oncological treatments, and the immunohistochemical marker CK7. Associations were found between age, grade, disease extent, and markers (CEA, CA19-9, AFP). <i>Conclusions:</i> Our findings suggest that [^99mTc]Tc-EDDA/HYNIC-TOC SPECT/CT effectively evaluates somatostatin receptor expression in NENs. Certain immunohistochemical and laboratory parameters, beyond recognized factors, show potential prognostic value, supporting individualized treatment strategies.

Fetal standard plane detection is essential in prenatal care, enabling accurate assessment of fetal development and early identification of potential anomalies. Despite significant advancements in machine learning (ML) in this domain, its integration into clinical workflows remains limited-primarily due to the lack of standardized, end-to-end operational frameworks. To address this gap, we introduce FetalMLOps, the first comprehensive MLOps framework specifically designed for fetal ultrasound imaging. Our approach adopts a ten-step MLOps methodology that covers the entire ML lifecycle, with each phase meticulously adapted to clinical needs. From defining the clinical objective to curating and annotating fetal US datasets, every step ensures alignment with real-world medical practice. ETL (extract, transform, load) processes are developed to standardize, anonymize, and harmonize inputs, enhancing data quality. Model development prioritizes architectures that balance accuracy and efficiency, using clinically relevant evaluation metrics to guide selection. The best-performing model is deployed via a RESTful API, following MLOps best practices for continuous integration, delivery, and performance monitoring. Crucially, the framework embeds principles of explainability and environmental sustainability, promoting ethical, transparent, and responsible AI. By operationalizing ML models within a clinically meaningful pipeline, FetalMLOps bridges the gap between algorithmic innovation and real-world application, setting a precedent for trustworthy and scalable AI adoption in prenatal care.

To evaluate the performance of artificial intelligence (AI)-based models in predicting elevated neonatal insulin levels through fetal hepatic echotexture analysis. This diagnostic accuracy study analyzed ultrasound images of fetal livers from pregnancies between 37 and 42 weeks, including cases with and without gestational diabetes mellitus (GDM). Images were stored in Digital Imaging and Communications in Medicine (DICOM) format, annotated by experts, and converted to segmented masks after quality checks. A balanced dataset was created by randomly excluding overrepresented categories. Artificial intelligence classification models developed using the FastAI library-ResNet-18, ResNet-34, ResNet-50, EfficientNet-B0, and EfficientNet-B7-were trained to detect elevated C-peptide levels (>75th percentile) in umbilical cord blood at birth, based on fetal hepatic ultrasonographic images. Out of 2339 ultrasound images, 606 were excluded due to poor quality, resulting in 1733 images analyzed. Elevated C-peptide levels were observed in 34.3% of neonates. Among the 5 CNN models evaluated, EfficientNet-B0 demonstrated the highest overall performance, achieving a sensitivity of 86.5%, specificity of 82.1%, positive predictive value (PPV) of 83.0%, negative predictive value (NPV) of 85.7%, accuracy of 84.3%, and an area under the ROC curve (AUC) of 0.83 in predicting elevated neonatal insulin levels through fetal hepatic echotexture analysis. AI-based analysis of fetal liver echotexture via ultrasound effectively predicted elevated neonatal C-peptide levels, offering a promising non-invasive method for detecting insulin imbalance in newborns.

Preterm birth is a major cause of mortality and lifelong morbidity in childhood. Its complex and multifactorial origins limit the effectiveness of current clinical predictors and impede optimal care. In this study, a dual-branch deep learning architecture (PUUMA) was developed to predict gestational age (GA) at birth using T2* fetal MRI data from 295 pregnancies, encompassing a heterogeneous and imbalanced population. The model integrates both global whole-uterus and local placental features. Its performance was benchmarked against linear regression using cervical length measurements obtained by experienced clinicians from anatomical MRI and other Deep Learning architectures. The GA at birth predictions were assessed using mean absolute error. Accuracy, sensitivity, and specificity were used to assess preterm classification. Both the fully automated MRI-based pipeline and the cervical length regression achieved comparable mean absolute errors (3 weeks) and good sensitivity (0.67) for detecting preterm birth, despite pronounced class imbalance in the dataset. These results provide a proof of concept for automated prediction of GA at birth from functional MRI, and underscore the value of whole-uterus functional imaging in identifying at-risk pregnancies. Additionally, we demonstrate that manual, high-definition cervical length measurements derived from MRI, not currently routine in clinical practice, offer valuable predictive information. Future work will focus on expanding the cohort size and incorporating additional organ-specific imaging to improve generalisability and predictive performance.