Artificial intelligence (AI) holds significant potential to enhance various aspects of oncology, spanning the cancer care continuum. This review provides an overview of current and emerging AI applications, from risk assessment and early detection to treatment and supportive care. AI-driven tools are being developed to integrate diverse data sources, including multi-omics and electronic health records, to improve cancer risk stratification and personalize prevention strategies. In screening and diagnosis, AI algorithms show promise in augmenting the accuracy and efficiency of medical image analysis and histopathology interpretation. AI also offers opportunities to refine treatment planning, optimize radiation therapy, and personalize systemic therapy selection. Furthermore, AI is explored for its potential to improve survivorship care by tailoring interventions and to enhance end-of-life care through improved symptom management and prognostic modeling. Beyond care delivery, AI augments clinical workflows, streamlines the dissemination of up-to-date evidence, and captures critical patient-reported outcomes for clinical decision support and outcomes assessment. However, the successful integration of AI into clinical practice requires addressing key challenges, including rigorous validation of algorithms, ensuring data privacy and security, and mitigating potential biases. Effective implementation necessitates interdisciplinary collaboration and comprehensive education for health care professionals. The synergistic interaction between AI and clinical expertise is crucial for realizing the potential of AI to contribute to personalized and effective cancer care. This review highlights the current state of AI in oncology and underscores the importance of responsible development and implementation.

We aimed to review the role of functional imaging in cervical cancer to underscore its significance in the diagnosis and management of cervical cancer and in improving patient outcomes. This rapid literature review targeting the clinical guidelines for functional imaging in cervical cancer sourced literature from 2017 to 2023 using PubMed, Google Scholar, MEDLINE and Scopus. Keywords such as cervical cancer, cervical neoplasms, functional imaging, stag*, treatment response, monitor* and New Zealand or NZ were used with Boolean operators to maximise results. Emphasis was on English full research studies pertinent to New Zealand. The study quality of the reviewed articles was assessed using the Joanna Briggs Institute critical appraisal checklists. The search yielded a total of 21 papers after all duplicates and yields that did not meet the inclusion criteria were excluded. Only one paper was found to incorporate the New Zealand context. The papers reviewed yielded results that demonstrate the important role of functional imaging in cervical cancer diagnosis, staging and treatment response monitoring. Techniques such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), diffusion-weighted magnetic resonance imaging (DW-MRI), computed tomography perfusion (CTP) and positron emission tomography computed tomography (PET/CT) provide deep insights into tumour behaviour, facilitating personalised care. Integration of artificial intelligence in image analysis promises increased accuracy of these modalities. Functional imaging could play a significant role in a unified approach in New Zealand to improve patient outcomes for cervical cancer management. Therefore, this study advocates for New Zealand's medical sector to harness functional imaging's potential in cervical cancer management.

Ultrasound imaging is critical for clinical diagnostics, providing insights into various diseases and organs. However, artificial intelligence (AI) in this field faces challenges, such as the need for large labeled datasets and limited task-specific model applicability, particularly due to ultrasound's low signal-to-noise ratio (SNR). To overcome these, we introduce the Ultrasound Representation Foundation Model (URFM), designed to learn robust, generalizable representations from unlabeled ultrasound images, enabling label-efficient adaptation to diverse diagnostic tasks. URFM is pre-trained on over 1M images from 15 major anatomical organs using representation-based masked image modeling (MIM), an advanced self-supervised learning. Unlike traditional pixel-based MIM, URFM integrates high-level representations from BiomedCLIP, a specialized medical vision-language model, to address the low SNR issue. Extensive evaluation shows that URFM outperforms state-of-the-art methods, offering enhanced generalization, label efficiency, and training-time efficiency. URFM's scalability and flexibility signal a significant advancement in diagnostic accuracy and clinical workflow optimization in ultrasound imaging.

Cortical morphological abnormalities in schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD) have been identified in past research. However, their potential as objective biomarkers to differentiate these disorders remains uncertain. Machine learning models may offer a novel diagnostic tool. Structural MRI (sMRI) of 220 SCZ, 220 MDD, 220 BD, and 220 healthy controls were obtained using a 3T scanner. Volume, thickness, surface area, and mean curvature of 68 cerebral cortices were extracted using FreeSurfer. 272 features underwent 3 feature selection techniques to isolate important variables for model construction. These features were incorporated into 3 classifiers for classification. After model evaluation and hyperparameter tuning, the best-performing model was identified, along with the most significant brain measures. The univariate feature selection-Naive Bayes model achieved the best performance, with an accuracy of 0.66, macro-average AUC of 0.86, and sensitivities and specificities ranging from 0.58-0.86 to 0.81-0.93, respectively. Key features included thickness of right isthmus-cingulate cortex, area of left inferior temporal gyrus, thickness of right superior temporal gyrus, mean curvature of right pars orbitalis, thickness of left transverse temporal cortex, volume of left caudal anterior-cingulate cortex, area of right banks superior temporal sulcus, and thickness of right temporal pole. The machine learning model based on sMRI data shows promise for aiding in the differential diagnosis of SCZ, MDD, and BD. Cortical features from the cingulate and temporal lobes may highlight distinct biological mechanisms underlying each disorder.

Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to assess physical properties of brain connections. Here, we present an integrative ecosystem of software that performs all steps of tractometry: post-processing of dMRI data, delineation of major white matter pathways, and modeling of the tissue properties within them. This ecosystem also provides a set of interoperable and extensible tools for visualization and interpretation of the results that extract insights from these measurements. These include novel machine learning and statistical analysis methods adapted to the characteristic structure of tract-based data. We benchmark the performance of these statistical analysis methods in different datasets and analysis tasks, including hypothesis testing on group differences and predictive analysis of subject age. We also demonstrate that computational advances implemented in the software offer orders of magnitude of acceleration. Taken together, these open-source software tools-freely available at https://tractometry.org-provide a transformative environment for the analysis of dMRI data.

Pituitary adenomas, ranging from subtle microadenomas to mass-effect macroadenomas, pose diagnostic challenges for radiologists due to increasing scan volumes and the complexity of dynamic contrast-enhanced MRI interpretation. A hybrid CNN-LSTM model was trained and validated on a multi-center dataset of 2,163 samples from Tehran and Babolsar, Iran. Transfer learning and preprocessing techniques (e.g., Wiener filters) were utilized to improve classification performance for microadenomas (< 10 mm) and macroadenomas (> 10 mm). The model achieved 90.5% accuracy, an area under the receiver operating characteristic curve (AUROC) of 0.92, and 89.6% sensitivity (93.5% for microadenomas, 88.3% for macroadenomas), outperforming standard CNNs by 5-18% across metrics. With a processing time of 0.17 s per scan, the model demonstrated robustness to variations in imaging conditions, including scanner differences and contrast variations, excelling in real-time detection and differentiation of adenoma subtypes. This dual-path approach, the first to synergize spatial and temporal MRI features for pituitary diagnostics, offers high precision and efficiency. Supported by comparisons with existing models, it provides a scalable, reproducible tool to improve patient outcomes, with potential adaptability to broader neuroimaging challenges.

This study aimed to develop and validate deep learning models using [¹⁸F]FDG PET/CT to predict PD-L1 status in esophageal cancer (EC) patients. Additionally, we assessed the potential of derived deep learning model scores (DLS) for survival stratification in immunotherapy. In this retrospective study, we included 331 EC patients from two centers, dividing them into training, internal validation, and external validation cohorts. Fifty patients who received immunotherapy were followed up. We developed four 3D ResNet10-based models-PET + CT + clinical factors (CPC), PET + CT (PC), PET (P), and CT (C)-using pre-treatment [¹⁸F]FDG PET/CT scans. For comparison, we also constructed a logistic model incorporating clinical factors (clinical model). The DLS were evaluated as radiological markers for survival stratification, and nomograms for predicting survival were constructed. The models demonstrated accurate prediction of PD-L1 status. The areas under the curve (AUCs) for predicting PD-L1 status were as follows: CPC (0.927), PC (0.904), P (0.886), C (0.934), and the clinical model (0.603) in the training cohort; CPC (0.882), PC (0.848), P (0.770), C (0.745), and the clinical model (0.524) in the internal validation cohort; and CPC (0.843), PC (0.806), P (0.759), C (0.667), and the clinical model (0.671) in the external validation cohort. The CPC and PC models exhibited superior predictive performance. Survival analysis revealed that the DLS from most models effectively stratified overall survival and progression-free survival at appropriate cut-off points (P < 0.05), outperforming stratification based on PD-L1 status (combined positive score ≥ 10). Furthermore, incorporating model scores with clinical factors in nomograms enhanced the predictive probability of survival after immunotherapy. Deep learning models based on [¹⁸F]FDG PET/CT can accurately predict PD-L1 status in esophageal cancer patients. The derived DLS can effectively stratify survival outcomes following immunotherapy, particularly when combined with clinical factors.

Fractional flow reserve (FFR) - a physiological indicator of coronary stenosis significance - has now become a widely used parameter also in the guidance of percutaneous coronary intervention (PCI). Several studies have shown the superiority of FFR compared to visual assessment, contributing to the reduction in clinical endpoints. However, the current approach to FFR assessment requires coronary instrumentation with a dedicated pressure wire and thus increasing invasiveness, cost, and duration of the procedure. Alternative, noninvasive methods of FFR assessment based on computational fluid dynamics are being widely tested; these approaches are generally not fully automated and may sometimes require substantial computational power. Nowadays, one of the most rapidly expanding fields in medicine is the use of artificial intelligence (AI) in therapy optimization, diagnosis, treatment, and risk stratification. AI usage contributes to the development of more sophisticated methods of imaging analysis and allows for the derivation of clinically important parameters in a faster and more accurate way. Over the recent years, AI utility in deriving FFR in a noninvasive manner has been increasingly reported. In this review, we critically summarize current knowledge in the field of AI-derived FFR based on data from computed tomography angiography, invasive angiography, optical coherence tomography, and intravascular ultrasound. Available solutions, possible future directions in optimizing cathlab performance, including the use of mixed reality, as well as current limitations standing behind the wide adoption of these techniques, are overviewed.

Spinal cord injury (SCI) in the pediatric population presents a unique challenge in diagnosis and prognosis due to the complexity of performing clinical assessments on children. Accurate evaluation of structural changes in the spinal cord is essential for effective treatment planning. This study aims to evaluate structural characteristics in pediatric patients with SCI by comparing cross-sectional area (CSA), anterior-posterior (AP) width, and right-left (RL) width across all vertebral levels of the spinal cord between typically developing (TD) and participants with SCI. We employed deep learning techniques to utilize these measures for detecting SCI cases and determining their injury severity. Sixty-one pediatric participants (ages 6-18), including 20 with chronic SCI and 41 TD, were enrolled and scanned by using a 3T MRI scanner. All SCI participants underwent the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) test to assess their neurologic function and determine their American Spinal Injury Association (ASIA) Impairment Scale (AIS) category. T2-weighted MRI scans were utilized to measure CSA, AP width, and RL widths along the entire cervical and thoracic cord. These measures were automatically extracted at every vertebral level of the spinal cord by using the spinal cord toolbox. Deep convolutional neural networks (CNNs) were utilized to classify participants into SCI or TD groups and determine their AIS classification based on structural parameters and demographic factors such as age and height. Significant differences (<i>P</i> < .05) were found in CSA, AP width, and RL width between SCI and TD participants, indicating notable structural alterations due to SCI. The CNN-based models demonstrated high performance, achieving 96.59% accuracy in distinguishing SCI from TD participants. Furthermore, the models determined AIS category classification with 94.92% accuracy. The study demonstrates the effectiveness of integrating cross-sectional structural imaging measures with deep learning methods for classification and severity assessment of pediatric SCI. The deep learning approach outperforms traditional machine learning models in diagnostic accuracy, offering potential improvements in patient care in pediatric SCI management.

Deep learning models often struggle to maintain generalizability in medical imaging, particularly under domain-fracture scenarios where distribution shifts arise from varying imaging techniques, acquisition protocols, patient populations, demographics, and equipment. In practice, each hospital may need to train distinct models - differing in learning task, width, and depth - to match local data. For example, one hospital may use Euclidean architectures such as MLPs and CNNs for tabular or grid-like image data, while another may require non-Euclidean architectures such as graph neural networks (GNNs) for irregular data like brain connectomes. How to train such heterogeneous models coherently across datasets, while enhancing each model's generalizability, remains an open problem. We propose unified learning, a new paradigm that encodes each model into a graph representation, enabling unification in a shared graph learning space. A GNN then guides optimization of these unified models. By decoupling parameters of individual models and controlling them through a unified GNN (uGNN), our method supports parameter sharing and knowledge transfer across varying architectures (MLPs, CNNs, GNNs) and distributions, improving generalizability. Evaluations on MorphoMNIST and two MedMNIST benchmarks - PneumoniaMNIST and BreastMNIST - show that unified learning boosts performance when models are trained on unique distributions and tested on mixed ones, demonstrating strong robustness to unseen data with large distribution shifts. Code and benchmarks: https://github.com/basiralab/uGNN