Lymphovascular invasion (LVI) is one of the most important prognostic factors in gastric cancer as it indicates a higher likelihood of lymph node metastasis and poorer overall outcome for the patient. Despite its importance, the detection of LVI(+) in histopathology specimens of gastric cancer can be a challenging task for pathologists as invasion can be subtle and difficult to discern. Herein, we propose a deep learning-based LVI(+) detection method using H&E-stained whole-slide images. The ConViT model showed the best performance in terms of both AUROC and AURPC among the classification models (AUROC: 0.9796; AUPRC: 0.9648). The AUROC and AUPRC of YOLOX computed based on the augmented patch-level confidence score were slightly lower (AUROC: −0.0094; AUPRC: −0.0225) than those of the ConViT classification model. With weighted averaging of the patch-level confidence scores, the ensemble model exhibited the best AUROC, AUPRC, and F1 scores of 0.9880, 0.9769, and 0.9280, respectively. The proposed model is expected to contribute to precision medicine by potentially saving examination-related time and labor and reducing disagreements among pathologists.

Increasing evidence indicates that sarcopenia and obesity can be risk factors for incident dementia. We investigated the association of body composition including muscle and visceral adipose tissue (VAT) with the probability of Alzheimer’s disease (AD) in healthy middle-aged and elderly subjects using ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT). This study included 110 healthy subjects with available whole-body FDG PET/CT scans and medical records. Muscle and VAT tissues were measured on the abdominal CT slice, and the PMOD Alzheimer’s discrimination FDG PET analysis tool (PALZ) score was evaluated on the brain PET of the same subject using software PALZ. Skeletal muscle index (r: −0.306; P = 0.031) was significantly negatively associated with the PALZ score in the elderly patients. Muscle area (β: −0.640; P = 0.043) and skeletal muscle index (β: −0.557; P = 0.043) were independently associated with the PALZ score in elderly subjects after adjustments for sex, duration of education, hypertension, diabetes mellitus, and smoking and drinking status. Increased muscle tissue was associated with a lower probability of AD in elderly subjects, but VAT was not associated with a lower probability of AD in middle-or older-aged adults.

Due to the increase of lung cancer globally, and particularly in Korea, survival analysis for this type of cancer has gained prominence in recent years. For this task, mathematical and traditional machine learning approaches are commonly used by medical doctors. While the deep learning approach has had proven success in computer vision tasks, natural language processing and other AI techniques are also adopted for this task. Due to the privacy issues and management process, data in medicine are difficult to collect, which leads to a paucity of samples. The small number of samples makes it difficult to use deep learning and renders this approach unusable. In this investigation, we propose a network architecture that combines a variational autoencoder (VAE) with the typical DNN architecture to solve the survival analysis task. With a training size of n = 4107, MVAESA achieves a C-index of $0.722$ while CoxCC, CoxPH, and CoxTime achieved scores of $0.713$, $0.703$, and $0.710$, respectively. With a small training size of n = 379, MVAESA achieves a C-index of $0.707$, compared with $0.689$, $0.688$ and $0.690$ for CoxCC, CoxPH, and CoxTime, respectively. The results show that the combination of a VAE with a target task makes the network more stable and that the network could be trained using a small-sized sample.

Accurate tissue segmentation is crucial for advancing healthcare, particularly in disease prediction and treatment planning. Precisely identifying abnormal tissue locations is a critical step for clinical analysis. While medical image segmentation increasingly utilizes multimodal and three-dimensional (3D) information to capture spatial relationships, current methods often struggle to effectively learn complementary information from multiple inputs, especially with complex 3D structures. In this study, we introduce AttCo, a novel multimodal semantic segmentation network built upon an attention-based co-learning fusion of deep feature representations. AttCo first employs multiple encoder branches to extract unimodal 3D representations from each imaging modality. These unimodal representations are then processed by a co-learning fusion module, which integrates both intra-modality (using SEAT) and inter-modality (using OSCAT) feature learning components. This dual approach ensures the capture of intricate interactions within each modality and across different modalities. Finally, these fused features pass through an up-sampling module to generate 3D segmented tumor maps. Our end-to-end deep network effectively addresses two key aspects: (i) extracting robust unimodal 3D representations and (ii) exploiting comprehensive inter- and intra-modality feature interactions. Experimental results demonstrate that AttCo significantly outperforms competing methods in terms of Dice score across various datasets. The source code can be found here https://github.com/duyphuongcri/AttCo.