Foundation models in medical imaging have shown promising label efficiency, achieving high downstream performance with only a fraction of annotated data. Here, we evaluate this in prostate multiparametric MRI using ProFound, a domain-specific vision foundation model pretrained on large-scale prostate MRI datasets. We investigate how variable image quality affects label-efficient finetuning by measuring the generalisability of finetuned models. Experiments systematically vary high-/low-quality image ratios in finetuning and evaluation sets. Our findings indicate that image quality distribution and its finetune-and-test mismatch significantly affect model performance. In particular: a) Varying the ratio of high- to low-quality images between finetuning and test sets leads to notable differences in downstream performance; and b) The presence of sufficient high-quality images in the finetuning set is critical for maintaining strong performance, whilst the importance of matched finetuning and testing distribution varies between different downstream tasks, such as automated radiology reporting and prostate cancer detection.When quality ratios are consistent, finetuning needs far less labeled data than training from scratch, but label efficiency depends on image quality distribution. Without enough high-quality finetuning data, pretrained models may fail to outperform those trained without pretraining. This highlights the importance of assessing and aligning quality distributions between finetuning and deployment, and the need for quality standards in finetuning data for specific downstream tasks. Using ProFound, we show the value of quantifying image quality in both finetuning and deployment to fully realise the data and compute efficiency benefits of foundation models.

Accurate prognostication and personalized treatment selection remain major challenges in kidney cancer. This consensus initiative aimed to provide actionable expert guidance on the development and clinical integration of prognostic and predictive biomarkers and risk stratification tools to improve patient care and guide future research. A modified Delphi method was employed to develop consensus statements among a multidisciplinary panel of experts in urologic oncology, medical oncology, radiation oncology, pathology, molecular biology, radiology, outcomes research, biostatistics, industry, and patient advocacy. Over 3 rounds, including an in-person meeting 20 initial statements were evaluated, refined, and voted on. Consensus was defined a priori as a median Likert score ≥8. Nineteen final consensus statements were endorsed. These span key domains including biomarker prioritization (favoring prognostic biomarkers), rigorous methodology for subgroup and predictive analyses, the development of multi-institutional prospective registries, incorporation of biomarkers in trial design, and improvements in data/biospecimen access. The panel also identified high-priority biomarker types (e.g., AI-based image analysis, ctDNA) for future research. This is the first consensus statement specifically focused on biomarker and risk model development for kidney cancer using a structured Delphi process. The recommendations emphasize the need for rigorous methodology, collaborative infrastructure, prospective data collection, and focus on clinically translatable biomarkers. The resulting framework is intended to guide researchers, cooperative groups, and stakeholders in advancing personalized care for patients with kidney cancer.

Abdominal haemorrhage is a life-threatening condition requiring prompt detection to enable timely intervention. Conventional ultrasound (US) is widely used but is highly operator-dependent, limiting its reliability outside clinical settings. In anatomical regions, in particular Morison's Pouch, US provides a higher detection reliability due to the preferential accumulation of free fluid in dependent areas. Recent advancements in artificial intelligence (AI)-integrated point-of-care US (POCUS) systems show promise for use in emergency, pre-hospital, military, and resource-limited environments. This systematic review evaluates the performance of AI-driven POCUS systems for detecting and estimating abdominal haemorrhage. A systematic search of Scopus, PubMed, EMBASE, and Web of Science (2014-2024) identified seven studies with sample sizes ranging from 94 to 6608 images and patient numbers ranging between 78 and 864 trauma patients. AI models, including YOLOv3, U-Net, and ResNet50, demonstrated high diagnostic accuracy, with sensitivity ranging from 88% to 98% and specificity from 68% to 99%. Most studies utilized 2D US imaging and conducted internal validation, typically employing systems such as the Philips Lumify and Mindray TE7. Model performance was predominantly assessed using internal datasets, wherein training and evaluation were performed on the same dataset. Of particular note, only one study validated its model on an independent dataset obtained from a different clinical setting. This limited use of external validation restricts the ability to evaluate the applicability of AI models across diverse populations and varying imaging conditions. Moreover, the Focused Assessment with Sonography in Trauma (FAST) is a protocol drive US method for detecting free fluid in the abdominal cavity, primarily in trauma cases. However, while it is commonly used to assess the right upper quadrant, particularly Morison's pouch, which is gravity-dependent and sensitive for early haemorrhage its application to other abdominal regions, such as the left upper quadrant and pelvis, remains underexplored. This is clinically significant, as fluid may preferentially accumulate in these areas depending on the mechanism of injury, patient positioning, or time since trauma, underscoring the need for broader anatomical coverage in AI applications. Researchers aiming to address the current reliance on 2D imaging and the limited use of external validation should focus future studies on integrating 3D imaging and utilising diverse, multicentre datasets to improve the reliability and generalizability of AI-driven POCUS systems for haemorrhage detection in trauma care.

Predicting the prognosis of bladder cancer remains challenging despite standard treatments. We developed an interpretable bladder cancer deep learning (BCDL) model using preoperative CT scans to predict overall survival. The model was trained on a cohort (n = 765) and validated in three independent cohorts (n = 438; n = 181; n = 72). The BCDL model outperformed other models in survival risk prediction, with the SHapley Additive exPlanation method identifying pixel-level features contributing to predictions. Patients were stratified into high- and low-risk groups using deep learning score cutoff. Adjuvant therapy significantly improved overall survival in high-risk patients (p = 0.028) and women in the low-risk group (p = 0.046). RNA sequencing analysis revealed differential gene expression and pathway enrichment between risk groups, with high-risk patients exhibiting an immunosuppressive microenvironment and altered microbial composition. Our BCDL model accurately predicts survival risk and supports personalized treatment strategies for improved clinical decision-making.

Foundation models in medical imaging have shown promising label efficiency, achieving high performance on downstream tasks using only a fraction of the annotated data otherwise required. In this study, we evaluate this potential in the context of prostate multiparametric MRI using ProFound, a recently developed domain-specific vision foundation model pretrained on large-scale prostate MRI datasets. We investigate the impact of variable image quality on the label-efficient finetuning, by quantifying the generalisability of the finetuned models. We conduct a comprehensive set of experiments by systematically varying the ratios of high- and low-quality images in the finetuning and evaluation sets. Our findings indicate that image quality distribution and its finetune-and-test mismatch significantly affect model performance. In particular: a) Varying the ratio of high- to low-quality images between finetuning and test sets leads to notable differences in downstream performance; and b) The presence of sufficient high-quality images in the finetuning set is critical for maintaining strong performance, whilst the importance of matched finetuning and testing distribution varies between different downstream tasks, such as automated radiology reporting and prostate cancer detection. Importantly, experimental results also show that, although finetuning requires significantly less labeled data compared to training from scratch when the quality ratio is consistent, this label efficiency is not independent of the image quality distribution. For example, we show cases that, without sufficient high-quality images in finetuning, finetuned models may fail to outperform those without pretraining.

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.

Colorectal cancer stands among the most prevalent digestive system malignancies. The microsatellite instability (MSI) profile plays a crucial role in determining patient outcomes and therapy responsiveness. Traditional MSI evaluation methods require invasive tissue sampling, are lengthy, and can be compromised by intratumoral heterogeneity. To establish a non-invasive technique utilizing dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and machine learning algorithms to determine MSI status in patients with intermediate-stage rectal cancer. This retrospective analysis examined 120 individuals diagnosed with stage II/III rectal cancer [30 MSI-high (MSI-H) and 90 microsatellite stability (MSS)/MSI-low (MSI-L) cases]. We extracted comprehensive radiomics signatures from DCE-MRI scans, encompassing textural parameters that reflect tumor heterogeneity, shape-based metrics, and histogram-derived statistical values. Least absolute shrinkage and selection operator regression facilitated feature selection, while predictive frameworks were developed using various classification algorithms (logistic regression, support vector machine, and random forest). Performance assessment utilized separate training and validation cohorts. Our investigation uncovered distinctive imaging characteristics between MSI-H and MSS/MSI-L neoplasms. MSI-H tumors exhibited significantly elevated entropy values (7.84 ± 0.92 <i>vs</i> 6.39 ± 0.83, <i>P</i> = 0.004), enhanced surface-to-volume proportions (0.72 ± 0.14 <i>vs</i> 0.58 ± 0.11, <i>P</i> = 0.008), and heightened signal intensity variation (3642 ± 782 <i>vs</i> 2815 ± 645, <i>P</i> = 0.007). The random forest model demonstrated superior classification capability with area under the curves (AUCs) of 0.891 and 0.896 across training and validation datasets, respectively. An integrated approach combining radiomics with clinical parameters further enhanced performance metrics (AUC 0.923 and 0.914), achieving 88.5% sensitivity alongside 87.2% specificity. DCE-MRI radiomics features interpreted through machine learning frameworks offer an effective strategy for MSI status assessment in intermediate-stage rectal cancer.

This study developed an end-to-end deep learning (DL) model using non-enhanced MRI to diagnose benign and malignant pelvic and sacral tumors (PSTs). Retrospective data from 835 patients across four hospitals were employed to train, validate, and test the models. Six diagnostic models with varied input sources were compared. Performance (AUC, accuracy/ACC) and reading times of three radiologists were compared. The proposed Model SEG-CL-NC achieved AUC/ACC of 0.823/0.776 (Internal Test Set 1) and 0.836/0.781 (Internal Test Set 2). In External Dataset Centers 2, 3, and 4, its ACC was 0.714, 0.740, and 0.756, comparable to contrast-enhanced models and radiologists (P > 0.05), while its diagnosis time was significantly shorter than radiologists (P < 0.01). Our results suggested that the proposed Model SEG-CL-NC could achieve comparable performance to contrast-enhanced models and radiologists in diagnosing benign and malignant PSTs, offering an accurate, efficient, and cost-effective tool for clinical practice.

BackgroundThe inbuilt ability to adapt existing models to new applications has been one of the key drivers of the success of deep learning models. Thereby, sharing trained models is crucial for their adaptation to different populations and domains. Not sharing models prohibits validation and potentially following translation into clinical practice, and hinders scientific progress. In this paper we examine the current state of data and model sharing in the medical field using cervical cancer staging on colposcopy images as a case example. MethodsWe conducted a comprehensive literature search in PubMed to identify studies employing machine learning techniques in the analysis of colposcopy images. For studies where raw data was not directly accessible, we systematically inquired about accessing the pre-trained model weights and/or raw colposcopy image data by contacting the authors using various channels. ResultsWe included 46 studies and one publicly available dataset in our study. We retrieved data of the latter and inquired about data access for the 46 studies by contacting a total of 92 authors. We received 15 responses related to 14 studies (30%). The remaining 32 studies remained unresponsive (70%). Of the 15 responses received, two responses redirected our inquiry to other authors, two responses were initially pending, and 11 declined data sharing. Despite our follow-up efforts on all responses received, none of the inquiries led to actual data sharing (0%). The only available data source remained the publicly available dataset. ConclusionsDespite the long-standing demands for reproducible research and efforts to incentivize data sharing, such as the requirement of data availability statements, our case study reveals a persistent lack of data sharing culture. Reasons identified in this case study include a lack of resources to provide the data, data privacy concerns, ongoing trial registrations and low response rates to inquiries. Potential routes for improvement could include comprehensive data availability statements required by journals, data preparation and deposition in a repository as part of the publication process, an automatic maximal embargo time after which data will become openly accessible and data sharing rules set by funders.

This study aimed to examine the performance of a fine-tuned large language model (LLM) in extracting pretreatment pancreatic cancer according to computed tomography (CT) radiology reports and to compare it with that of readers. This retrospective study included 2690, 886, and 378 CT reports for the training, validation, and test datasets, respectively. Clinical indication, image finding, and imaging diagnosis sections of the radiology report (used as input data) were reviewed and categorized into groups 0 (no pancreatic cancer), 1 (after treatment for pancreatic cancer), and 2 (pretreatment pancreatic cancer present) (used as reference data). A pre-trained Bidirectional Encoder Representation from the Transformers Japanese model was fine-tuned with the training and validation dataset. Group 1 data were undersampled and group 2 data were oversampled in the training dataset due to group imbalance. The best-performing model from the validation set was subsequently assessed using the test dataset for testing purposes. Additionally, three readers (readers 1, 2, and 3) were involved in classifying reports within the test dataset. The fine-tuned LLM and readers 1, 2, and 3 demonstrated an overall accuracy of 0.942, 0.984, 0.979, and 0.947; sensitivity for differentiating groups 0/1/2 of 0.944/0.960/0.921, 0.976/1.000/0.976, 0.984/0.984/0.968, and 1.000/1.000/0.841; and total time required for classification of 49 s, 2689 s, 3496 s, and 4887 s, respectively. Fine-tuned LLM effectively extracted patients with pretreatment pancreatic cancer according to CT radiology reports, and its performance was comparable to that of readers in a shorter time.