This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) for the preoperative evaluation of axillary lymph node (ALN) metastasis status in patients with a newly diagnosed unifocal breast cancer. A total of 883 eligible patients with breast cancer who underwent preoperative breast and axillary ultrasound were retrospectively enrolled between April 1, 2016, and June 30, 2022. The training cohort comprised 621 patients from Hospital I; the external validation cohorts comprised 112, 87, and 63 patients from Hospitals II, III, and IV, respectively. A DLR signature was created based on the deep learning and handcrafted features, and the DLRN was then developed based on the signature and four independent clinical parameters. The DLRN exhibited good performance, yielding areas under the receiver operating characteristic curve (AUC) of 0.914, 0.929, and 0.952 in the three external validation cohorts, respectively. Decision curve and calibration curve analyses demonstrated the favorable clinical value and calibration of the nomogram. In addition, the DLRN outperformed five experienced radiologists in all cohorts. This has the potential to guide appropriate management of the axilla in patients with breast cancer, including avoiding overtreatment.

The presence of tertiary lymphoid structures (TLSs) on pancreatic pathological images is an important prognostic indicator of pancreatic tumors. Therefore, TLSs detection on pancreatic pathological images plays a crucial role in diagnosis and treatment for patients with pancreatic tumors. However, fully supervised detection algorithms based on deep learning usually require a large number of manual annotations, which is time-consuming and labor-intensive. In this paper, we aim to detect the TLSs in a manner of few-shot learning by proposing a weakly supervised segmentation network. We firstly obtain the lymphocyte density maps by combining a pretrained model for nuclei segmentation and a domain adversarial network for lymphocyte nuclei recognition. Then, we establish a cross-scale attention guidance mechanism by jointly learning the coarse-scale features from the original histopathology images and fine-scale features from our designed lymphocyte density attention. A noise-sensitive constraint is introduced by an embedding signed distance function loss in the training procedure to reduce tiny prediction errors. Experimental results on two collected datasets demonstrate that our proposed method significantly outperforms the state-of-the-art segmentation-based algorithms in terms of TLSs detection accuracy. Additionally, we apply our method to study the congruent relationship between the density of TLSs and peripancreatic vascular invasion and obtain some clinically statistical results.

In medical image analysis, because of the noises, low contrast, complex textures and inhomogeneities, robust segmentation for objects is a challenging problem. In this paper, a modified variational model is proposed to improve segmentation effects in medical images by separating the regions of interest (ROIs) and backgrounds based on both intensity and texture features. Information from these two sources are fused by the Dempster-Shafer evidence theory and to provide a compelling evidence for object segmentation. Since it can capture the differences of textures as well as intensities between the object and background, the proposed method is effective on segmentation of lesions, even lesion areas that have similar intensities with their surroundings. To prove the efficiency of the proposed method, 52 renal ultrasound images with low contrast and inhomogeneous lesions are utilized in the quantitative evaluations. Compared with the classical CV model, the method using a new distribution metric based on prediction theory, the method using Bayesian theorem to fuse information, the MICO method and RSF method, the proposed method has a mean precision of 94.7%, a mean Dice coefficient of 92.8%, being the highest among all the tested methods, and the mean absolute surface distance (MAD) and the symmetric mean absolute surface distance (SMAD) of this method are 0.11 and 0.13, respectively, which are lower than those of other methods. Comparison results demonstrate that segmentation results by the proposed method are closer to ground truths than those of other methods.

Tumor microenvironment (TME) analysis plays an extremely important role in computational pathology. Deep learning shows tremendous potential for tumor tissue segmentation on pathological images, which is an essential part of TME analysis. However, fully supervised segmentation methods based on deep learning usually require a large number of manual annotations, which is time-consuming and labor-intensive. Recently, weakly supervised semantic segmentation (WSSS) works based on the Class Activation Map (CAM) have shown promising results to learn the concept of segmentation from image-level class labels but usually have imprecise boundaries due to the lack of pixel-wise supervision. On the other hand, the Segment Anything Model (SAM), a foundation model for segmentation, has shown an impressive ability for general semantic segmentation on natural images, while it suffers from the noise caused by the initial prompts. To address these problems, we propose a simple but effective weakly supervised framework, termed as 2AM, combining CAM and SAM for tumor tissue segmentation on pathological images. Our 2AM model is composed of three modules: (1) a CAM module for generating salient regions for tumor tissues on pathological images; (2) an adaptive point selection (APS) module for providing more reliable initial prompts for the subsequent SAM by designing three priors of basic appearance, space distribution, and feature difference; and (3) a SAM module for predicting the final segmentation. Experimental results on two independent datasets show that our proposed method boosts tumor segmentation accuracy by nearly 25% compared with the baseline method, and achieves more than 15% improvement compared with previous state-of-the-art segmentation methods with WSSS settings.