Intratumor heterogeneity (ITH) profoundly affects therapeutic responses and clinical outcomes. However, the widespread methods for assessing ITH based on genomic sequencing or pathological slides, which rely on limited tissue samples, may lead to inaccuracies due to potential sampling biases. Using a newly established multicenter breast cancer radio-multiomic dataset ( n = 1474) encompassing radiomic features extracted from dynamic contrast–enhanced magnetic resonance images, we formulated a noninvasive radiomics methodology to effectively investigate ITH. Imaging ITH (IITH) was associated with genomic and pathological ITH, predicting poor prognosis independently in breast cancer. Through multiomic analysis, we identified activated oncogenic pathways and metabolic dysregulation in high-IITH tumors. Integrated metabolomic and transcriptomic analyses highlighted ferroptosis as a vulnerability and potential therapeutic target of high-IITH tumors. Collectively, this work emphasizes the superiority of radiomics in capturing ITH. Furthermore, we provide insights into the biological basis of IITH and propose therapeutic targets for breast cancers with elevated IITH.

Accurate grading of liver fibrosis can effectively assess the severity of liver disease and help doctors make an appropriate diagnosis. This study aimed to perform the automatic staging of hepatic fibrosis on patients with hepatitis B, who underwent gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging with dynamic radiomics analysis. The proposed dynamic radiomics model combined imaging features from multi-phase dynamic contrast-enhanced (DCE) images and time-domain information. Imaging features were extracted from the deep learning-based segmented liver volume, and time-domain features were further explored to analyze the variation in features during contrast enhancement. Model construction and evaluation were based on a 132-case data set. The proposed model achieved remarkable performance in significant fibrosis (fibrosis stage S1 vs. S2–S4; accuracy (ACC) = 0.875, area under the curve (AUC) = 0.867), advanced fibrosis (S1–S2 vs. S3–S4; ACC = 0.825, AUC = 0.874), and cirrhosis (S1–S3 vs. S4; ACC = 0.850, AUC = 0.900) classifications in the test set. It was more dominant compared with the conventional single-phase or multi-phase DCE-based radiomics models, normalized liver enhancement, and some serological indicators. Time-domain features were found to play an important role in the classification models. The dynamic radiomics model can be applied for highly accurate automatic hepatic fibrosis staging.

Liver tumor semantic segmentation is a crucial task in medical image analysis that requires multiple MRI modalities. This paper proposes a novel coarse-to-fine fusion segmentation approach to detect and segment small liver tumors of various sizes. To enhance the segmentation accuracy of small liver tumors, the method incorporates a detection module and a CSR (convolution-SE-residual) module, which includes a convolution block, an SE (squeeze and excitation) module, and a residual module for fine segmentation. The proposed method demonstrates superior performance compared to conventional single-stage end-to-end networks. A private liver MRI dataset comprising 218 patients with a total of 3605 tumors, including 3273 tumors smaller than 3.0 cm, were collected for the proposed method. There are five types of liver tumors identified in this dataset: hepatocellular carcinoma (HCC); metastases of the liver; cholangiocarcinoma (ICC); hepatic cyst; and liver hemangioma. The results indicate that the proposed method outperforms the single segmentation networks 3D UNet and nnU-Net as well as the fusion networks of 3D UNet and nnU-Net with nnDetection. The proposed architecture was evaluated on a test set of 44 images, with an average Dice similarity coefficient (DSC) and recall of 86.9% and 86.7%, respectively, which is a 1% improvement compared to the comparison method. More importantly, compared to existing methods, our proposed approach demonstrates state-of-the-art performance in segmenting small objects with sizes smaller than 10 mm, achieving a Dice score of 85.3% and a malignancy detection rate of 87.5%.

Diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging are essential for the diagnosis of hepatocellular carcinoma (HCC) but suffer from severe displacement due to respiratory and cardiac motions. The currently available registration algorithms do not align the multi-contrast liver images satisfactorily, which hinders the efficacy of automatic lesion segmentation and radiomics analysis. We proposed a deep learning-based generative adversarial registration network (GARNET) for multi-contrast liver image registration and explored the added value on HCC lesion segmentation in patients with cirrhosis. The proposed model applied a pre-trained generative adversarial network to synthesize DCE images from DWI and then used diffeomorphic based symmetric image normalization (SyN) algorithm for registration. Furthermore, an attention-based U-net (AU-NET) was used to segment the HCC lesion from registered DWI and DCE images. GARNET was developed and validated on 901 patients collected between January 2013 and February 2021 across five medical centers, including 517 HCC patients, total of 597 HCCs, and every case was interpolated to 32 slices. GARNET achieved the highest liver registration Dice (0.812 ± 0.045) among all comparative methods, and the lowest tumor target registration error (3.470 ± 1.652 mm) for DWI/DCE registrations. In addition, the performance of HCC segmentation was significantly improved when using registered images from GARNET, with Dice improvements from 0.36 ± 0.22 to 0.45 ± 0.22; 0.53 ± 0.25 to 0.58 ± 0.21; and 0.44 ± 0.26 to 0.56 ± 0.19 in three validation centers. Experimental results showed that GARNET significantly improved the liver registration performances among multi-contrast images, thereby improving the further identification and segmentation of HCCs.

Predicting stroke outcome remains challenging due to inherent heterogeneity, misalignment of multimodal clinical data, and the availability of well-annotated longitudinal datasets. Current methodologies often lack robustness and generalizability across these tasks. We propose a few-shot contrastive learning framework that integrates brain MRI images and structured clinical records for cross-task prognosis prediction, addressing both morphological and functional outcomes. Our method combines Model-Agnostic Meta-Learning (MAML) with a two-step contrastive learning strategy including self-awareness learning that captures task-specific features and domain learning that facilitates cross-dataset generalization. To handle inconsistencies in tabular data, a Misalignment Separation technique was adopted. The framework jointly trains a domain encoder on multimodal inputs, capturing shared and task-specific prior knowledge to enhance predictive robustness. Evaluations on 309 patients for morphological outcome and 341 patients for functional outcome, as well as on external validation datasets, demonstrated that our approach outperformed SimCLR and conventional supervised methods, and could effectively integrate cross-task datasets. This framework highlights the potential of multimodal few-shot learning for robust stroke prognosis prediction for small-sample datasets.