Pixel-wise annotations are notoriously labourious and costly to obtain in the medical domain. To mitigate this burden, weakly supervised approaches based on bounding box annotations-much easier to acquire-offer a practical alternative. Vision foundation models have recently shown noteworthy segmentation performance when provided with prompts such as points or bounding boxes. Prompt learning exploits these models by adapting them to downstream tasks and automating segmentation, thereby reducing user intervention. However, existing prompt learning approaches depend on fully annotated segmentation masks. This paper proposes a novel framework that combines the representational power of foundation models with the annotation efficiency of weakly supervised segmentation. More specifically, our approach automates prompt generation for foundation models using only bounding box annotations. Our proposed optimization scheme integrates multiple constraints derived from box annotations with pseudo-labels generated by the prompted foundation model. Extensive experiments across multi-modal datasets reveal that our weakly supervised method achieves an average Dice score of 84.90% in a limited data setting, outperforming existing fully-supervised and weakly-supervised approaches. The code will be available upon acceptance.

This study aimed to develop a deep learning (DL) model based on three-dimensional multi-parametric magnetic resonance imaging (mpMRI) for preoperative assessment of lymph node metastasis (LNM) in rectal cancer (RC) and to investigate the contribution of different MRI sequences. A total of 613 eligible patients with RC from four medical centres who underwent preoperative mpMRI were retrospectively enrolled and randomly assigned to training (n = 372), validation (n = 106), internal test (n = 88) and external test (n = 47) cohorts. A multi-parametric multi-scale EfficientNet (MMENet) was designed to effectively extract LNM-related features from mpMR for preoperative LNM assessment. Its performance was compared with other DL models and radiologists using metrics of area under the receiver operating curve (AUC), accuracy (ACC), sensitivity, specificity and average precision with 95% confidence interval (CI). To investigate the utility of various MRI sequences, the performances of the mono-parametric model and the MMENet with different sequences combinations as input were compared. The MMENet using a combination of T2WI, DWI and DCE sequence achieved an AUC of 0.808 (95% CI 0.720-0.897) with an ACC of 71.6% (95% CI 62.3-81.0) in the internal test cohort and an AUC of 0.782 (95% CI 0.636-0.925) with an ACC of 76.6% (95% CI 64.6-88.6) in the external test cohort, outperforming the mono-parametric model, the MMENet with other sequences combinations and the radiologists. The MMENet, leveraging a combination of T2WI, DWI and DCE sequences, can accurately assess LNM in RC preoperatively and holds great promise for automated evaluation of LNM in clinical practice.

Subarachnoid hemorrhage (SAH) is a severe condition with high morbidity and long-term neurological consequences. Radiomics, by extracting quantitative features from Computed Tomograhpy (CT) scans, may reveal imaging biomarkers predictive of outcomes. This study evaluates the predictive value of radiomics in SAH for multiple outcomes and compares its performance to models based on clinical data.Radiomic features were extracted from admission CTs using segmentations of brain tissue (white and gray matter) and hemorrhage. Machine learning models with cross-validation were trained using clinical data, radiomics, or both, to predict 6-month mortality, Glasgow Outcome Scale (GOS), vasospasm, and long-term hydrocephalus. SHapley Additive exPlanations (SHAP) analysis was used to interpret feature contributions.The training dataset included 403 aneurysmal SAH patients; GOS predictions used all patients, while vasospasm and hydrocephalus predictions excluded those with incomplete data or early death, leaving 328 and 332 patients, respectively. Radiomics and clinical models demonstrated comparable performance, achieving in validation set AUCs more than 85% for six-month mortality and clinical outcome, and 75% and 86% for vasospasm and hydrocephalus, respectively. In an independent cohort of 41 patients, the combined models yielded AUCs of 89% for mortality, 87% for clinical outcome, 66% for vasospasm, and 72% for hydrocephalus. SHAP analysis highlighted significant contributions of radiomic features from brain tissue and hemorrhage segmentation, alongside key clinical variables, in predicting SAH outcomes.This study underscores the potential of radiomics-based approaches for SAH outcome prediction, demonstrating predictive power comparable to traditional clinical models and enhancing understanding of SAH-related complications.Clinical trial number Not applicable.

To evaluate PANCANAI, a previously developed AI model for pancreatic cancer (PC) detection, on a longitudinal cohort of patients. In particular, aiming for PC detection on scans acquired before histopathologic diagnosis was assessed. The model has been previously trained to predict PC suspicion on 2134 portal venous CTs. In this study, the algorithm was evaluated on a retrospective cohort of Danish patients with biopsy-confirmed PC and with CT scans acquired between 2006 and 2016. The sensitivity was measured, and bootstrapping was performed to provide median and 95% CI. The study included 1083 PC patients (mean age: 69 y ± 11, 575 men). CT scans were divided into 2 groups: (1) concurrent diagnosis (CD): 1022 CT scans acquired within 2 months around histopathologic diagnosis, and (2) prediagnosis (PD): 198 CT scans acquired before histopathologic diagnosis (median 7 months before diagnosis). The sensitivity was 91.8% (938 of 1022; 95% CI: 89.9-93.5) and 68.7% (137 of 198; 95% CI: 62.1-75.3) on the CD and PD groups, respectively. Sensitivity on CT scans acquired 1 year or more before diagnosis was 53.9% (36 of 67; 95% CI: 41.8-65.7). Sensitivity on CT scans acquired at stage I was 82.9% (29 of 35; 95% CI: 68.6-94.3). PANCANAI showed high sensitivity for automatic PC detection on a large retrospective cohort of biopsy-confirmed patients. PC suspicion was detected in more than half of the CT scans that were acquired at least a year before histopathologic diagnosis.

Laser interstitial thermal therapy (LiTT) has emerged as a minimally invasive, MRI-guided treatment of brain tumors that are otherwise considered inoperable because of their location or the patient's poor surgical candidacy. By directing thermal energy at neoplastic lesions while minimizing damage to surrounding healthy tissue, LiTT offers promising therapeutic outcomes for both newly diagnosed and recurrent tumors. However, challenges such as postprocedural edema, unpredictable heat diffusion near blood vessels and ventricles in real time underscore the need for improved planning and monitoring. Incorporating artificial intelligence (AI) presents a viable solution to many of these obstacles. AI has already demonstrated effectiveness in optimizing surgical trajectories, predicting seizure-free outcomes in epilepsy cases, and generating heat distribution maps to guide real-time ablation. This technology could be similarly deployed in neurosurgical oncology to identify patients most likely to benefit from LiTT, refine trajectory planning, and predict tissue-specific heat responses. Despite promising initial studies, further research is needed to establish the robust data sets and clinical trials necessary to develop and validate AI-driven LiTT protocols. Such advancements have the potential to bolster LiTT's efficacy, minimize complications, and ultimately transform the neurosurgical management of primary and metastatic brain tumors.

Radiomics analyzes quantitative features from medical images to reveal tumor heterogeneity, offering new insights for diagnosis, prognosis, and treatment prediction. This study explored radiomics based biomarkers to predict immunotherapy response and its association with the tumor microenvironment in non-small cell lung cancer (NSCLC) using unsupervised machine learning models derived from CT imaging. This study included 1539 NSCLC patients from seven independent cohorts. For 1834 radiomic features extracted from 869 NSCLC patients, K-means unsupervised clustering was applied to identify radiomic subtypes. A random forest model extended subtype classification to external cohorts, model accuracy, sensitivity, and specificity were evaluated. By conducting bulk RNA sequencing (RNA-seq) and single-cell transcriptome sequencing (scRNA-seq) of tumors, the immune microenvironment characteristics of tumors can be obtained to evaluate the association between radiomic subtypes and immunotherapy efficacy, immune scores, and immune cells infiltration. Unsupervised clustering stratified NSCLC patients into two subtypes (Cluster 1 and Cluster 2). Principal component analysis confirmed significant distinctions between subtypes across all cohorts. Cluster 2 exhibited significantly longer median overall survival (35 vs. 30 months, P = 0.006) and progression-free survival (19 vs. 16 months, P = 0.020) compared to Cluster 1. Multivariate Cox regression identified radiomic subtype as an independent predictor of overall survival (HR: 0.738, 95% CI 0.583-0.935, P = 0.012), validated in two external cohorts. Bulk RNA seq showed elevated interaction signaling and immune scores in Cluster 2 and scRNA-seq demonstrated higher proportions of T cells, B cells, and NK cells in Cluster 2. This study establishes a radiomic subtype associated with NSCLC immunotherapy efficacy and tumor immune microenvironment. The findings provide a non-invasive tool for personalized treatment, enabling early identification of immunotherapy-responsive patients and optimized therapeutic strategies.

The clinical utility of the DeepSeek-V3 (DSV3) model in enhancing the accuracy of Liver Imaging Reporting and Data System (LI-RADS, LR) classification remains underexplored. This study aimed to evaluate the diagnostic performance of DSV3 in LR classifications compared to radiologists with varying levels of experience and to assess its potential as a decision-support tool in clinical practice. A dual-phase retrospective-prospective study analyzed 426 liver lesions (300 retrospective, 126 prospective) in high-risk HCC patients who underwent Magnetic Resonance Imaging (MRI) or Computed Tomography (CT). Three radiologists (one junior, two seniors) independently classified lesions using LR v2018 criteria, while DSV3 analyzed unstructured radiology reports to generate corresponding classifications. In the prospective cohort, DSV3 processed inputs in both Chinese and English to evaluate language impact. Performance was compared using chi-square test or Fisher's exact test, with pathology as the gold standard. In the retrospective cohort, DSV3 significantly outperformed junior radiologists in diagnostically challenging categories: LR-3 (17.8% vs. 39.7%, p<0.05), LR-4 (80.4% vs. 46.2%, p<0.05), and LR-5 (86.2% vs. 66.7%, p<0.05), while showing comparable accuracy in LR-1 (90.8% vs. 88.7%), LR-2 (11.9% vs. 25.6%), and LR-M (79.5% vs. 62.1%) classifications (all p>0.05). Prospective validation confirmed these findings, with DSV3 demonstrating superior performance for LR-3 (13.3% vs. 60.0%), LR-4 (93.3% vs. 66.7%), and LR-5 (93.5% vs. 67.7%) compared to junior radiologists (all p<0.05). Notably, DSV3 achieved diagnostic parity with senior radiologists across all categories (p>0.05) and maintained consistent performance between Chinese and English inputs. The DSV3 model effectively improves diagnostic accuracy of LR-3 to LR-5 classifications among junior radiologists . Its language-independent performance and ability to match senior-level expertise suggest strong potential for clinical implementation to standardize HCC diagnosis and optimize treatment decisions.

Brain ultrasound is a popular point-of-care test that helps visualize brain structures. This review highlights recent developments in brain ultrasonography. There is a need to keep pace with the ongoing technological advancements and establishing standardized quality criteria for improving its utility in clinical practice. Newer automated indices derived from transcranial Doppler help establish its role as a noninvasive monitor of intracranial pressure and diagnosing vasospasm/delayed cerebral ischemia. A novel robotic transcranial Doppler system equipped with artificial intelligence allows real-time continuous neuromonitoring. Intraoperative ultrasound assists neurosurgeons in real-time localization of brain lesions and helps in assessing the extent of resection, thereby enhancing surgical precision and safety. Optic nerve sheath diameter point-of-care ultrasonography is an effective means of diagnosing raised intracranial pressure, triaging, and prognostication. The quality criteria checklist can help standardize this technique. Newer advancements like focused ultrasound, contrast-enhanced ultrasound, and functional ultrasound have also been discussed. Brain ultrasound continues to be a critical bedside tool in neurologically injured patients. With the advent of technological advancements, its utility has widened and its capabilities have expanded, making it more accurate and versatile in clinical practice.

Early detection through screening is critical for reducing gastric cancer (GC) mortality. However, in most high-prevalence regions, large-scale screening remains challenging due to limited resources, low compliance and suboptimal detection rate of upper endoscopic screening. Therefore, there is an urgent need for more efficient screening protocols. Noncontrast computed tomography (CT), routinely performed for clinical purposes, presents a promising avenue for large-scale designed or opportunistic screening. Here we developed the Gastric Cancer Risk Assessment Procedure with Artificial Intelligence (GRAPE), leveraging noncontrast CT and deep learning to identify GC. Our study comprised three phases. First, we developed GRAPE using a cohort from 2 centers in China (3,470 GC and 3,250 non-GC cases) and validated its performance on an internal validation set (1,298 cases, area under curve = 0.970) and an independent external cohort from 16 centers (18,160 cases, area under curve = 0.927). Subgroup analysis showed that the detection rate of GRAPE increased with advancing T stage but was independent of tumor location. Next, we compared the interpretations of GRAPE with those of radiologists and assessed its potential in assisting diagnostic interpretation. Reader studies demonstrated that GRAPE significantly outperformed radiologists, improving sensitivity by 21.8% and specificity by 14.0%, particularly in early-stage GC. Finally, we evaluated GRAPE in real-world opportunistic screening using 78,593 consecutive noncontrast CT scans from a comprehensive cancer center and 2 independent regional hospitals. GRAPE identified persons at high risk with GC detection rates of 24.5% and 17.7% in 2 regional hospitals, with 23.2% and 26.8% of detected cases in T1/T2 stage. Additionally, GRAPE detected GC cases that radiologists had initially missed, enabling earlier diagnosis of GC during follow-up for other diseases. In conclusion, GRAPE demonstrates strong potential for large-scale GC screening, offering a feasible and effective approach for early detection. ClinicalTrials.gov registration: NCT06614179 .

This study aimed to develop and validate a multimodal deep learning model that leverages 2D grayscale ultrasound (US) images alongside readily available clinical data to improve diagnostic performance for ovarian cancer (OC). A retrospective analysis was conducted involving 1899 patients who underwent preoperative US examinations and subsequent surgeries for adnexal masses between 2019 and 2024. A multimodal deep learning model was constructed for OC diagnosis and extracting US morphological features from the images. The model's performance was evaluated using metrics such as receiver operating characteristic (ROC) curves, accuracy, and F1 score. The multimodal deep learning model exhibited superior performance compared to the image-only model, achieving areas under the curves (AUCs) of 0.9393 (95% CI 0.9139-0.9648) and 0.9317 (95% CI 0.9062-0.9573) in the internal and external test sets, respectively. The model significantly improved the AUCs for OC diagnosis by radiologists and enhanced inter-reader agreement. Regarding US morphological feature extraction, the model demonstrated robust performance, attaining accuracies of 86.34% and 85.62% in the internal and external test sets, respectively. Multimodal deep learning has the potential to enhance the diagnostic accuracy and consistency of radiologists in identifying OC. The model's effective feature extraction from ultrasound images underscores the capability of multimodal deep learning to automate the generation of structured ultrasound reports.