An omics-to-omics joint knowledge association subtensor model for radiogenomics cross-modal modules from genomics and ultrasonic images of breast cancers

dbCRAF: a curated knowledgebase for regulation of radiation response in human cancer

Radiation therapy (RT) is one of the primary treatment modalities of cancer, with 40-60% of cancer patients benefiting from RT during their treatment course. The intrinsic radiosensitivity or acquired radioresistance of tumor cells would affect the response to RT and clinical outcomes in patients. Thus, mining the regulatory mechanisms in tumor radiosensitivity or radioresistance that have been verified by biological experiments and computational analysis methods will enhance the overall understanding of RT. Here, we describe a comprehensive database dbCRAF (http://dbCRAF.xialab.info/) to document and annotate the factors (1,677 genes, 49 proteins and 612 radiosensitizers) linked with radiation response, including radiosensitivity, radioresistance in cancer cells and prognosis in cancer patients receiving RT. On the one hand, dbCRAF enables researchers to directly access knowledge for regulation of radiation response in human cancer buried in the vast literature. On the other hand, dbCRAF provides four flexible modules to analyze and visualize the functional relationship between these factors and clinical outcome, KEGG pathway and target genes. In conclusion, dbCRAF serves as a valuable resource for elucidating the regulatory mechanisms of radiation response in human cancers as well as for the improvement of RT options.

Exploring the Application of the Artificial-Intelligence-Integrated Platform 3D Slicer in Medical Imaging Education

Artificial Intelligence (AI) has revolutionized medical imaging procedures, specifically with regard to image segmentation, reconstruction, interpretation, and research. 3D Slicer, an open-source medical image analysis platform, has become a valuable tool in medical imaging education due to its integration of various AI applications. Through its open-source architecture, students can gain practical experience with diverse medical images and the latest AI technology, reinforcing their understanding of anatomy and imaging technology while fostering independent learning and clinical reasoning skills. The implementation of this platform improves instruction quality and nurtures skilled professionals who can meet the demands of clinical practice, research institutions, and technology innovation enterprises. AI algorithms' application in medical image processing have facilitated their translation from the lab to practical clinical applications and education.

Leveraging Summary Guidance on Medical Report Summarization

This study presents three deidentified large medical text datasets, named DISCHARGE, ECHO and RADIOLOGY, which contain 50 K, 16 K and 378 K pairs of report and summary that are derived from MIMIC-III, respectively. We implement convincing baselines of automated abstractive summarization on the created datasets with pre-trained encoder-decoder language models, including BERT2BERT, BERTShare, RoBERTaShare, Pegasus, ProphetNet, T5-large, BART and GSUM. Further, based on the BART model, we leverage the sampled summaries from the training set as prior knowledge guidance, for encoding additional contextual representations of the guidance with the encoder and enhancing the decoding representations in the decoder. The experimental results confirm the improvement of ROUGE scores and BERTScore made by the proposed method.

Knowledge Tensor-Aided Breast Ultrasound Image Assistant Inference Framework

Breast cancer is one of the most prevalent cancers in women nowadays, and medical intervention at an early stage of cancer can significantly improve the prognosis of patients. Breast ultrasound (BUS) is a widely used tool for the early screening of breast cancer in primary care hospitals but it relies heavily on the ability and experience of physicians. Accordingly, we propose a knowledge tensor-based Breast Imaging Reporting and Data System (BI-RADS)-score-assisted generalized inference model, which uses the BI-RADS score of senior physicians as the gold standard to construct a knowledge tensor model to infer the benignity and malignancy of breast tumors and axes the diagnostic results against those of junior physicians to provide an aid for breast ultrasound diagnosis. The experimental results showed that the diagnostic AUC of the knowledge tensor constructed using the BI-RADS characteristics labeled by senior radiologists achieved 0.983 (95% confidential interval (CI) = 0.975-0.992) for benign and malignant breast cancer, while the diagnostic performance of the knowledge tensor constructed using the BI-RADS characteristics labeled by junior radiologists was only 0.849 (95% CI = 0.823-0.876). With the knowledge tensor fusion, the AUC is improved to 0.887 (95% CI = 0.864-0.909). Therefore, our proposed knowledge tensor can effectively help reduce the misclassification of BI-RADS characteristics by senior radiologists and, thus, improve the diagnostic performance of breast-ultrasound-assisted diagnosis.

Enhancing genomic mutation data storage optimization based on the compression of asymmetry of sparsity

Background: With the rapid development of high-throughput sequencing technology and the explosive growth of genomic data, storing, transmitting and processing massive amounts of data has become a new challenge. How to achieve fast lossless compression and decompression according to the characteristics of the data to speed up data transmission and processing requires research on relevant compression algorithms. Methods: In this paper, a compression algorithm for sparse asymmetric gene mutations (CA_SAGM) based on the characteristics of sparse genomic mutation data was proposed. The data was first sorted on a row-first basis so that neighboring non-zero elements were as close as possible to each other. The data were then renumbered using the reverse Cuthill-Mckee sorting technique. Finally the data were compressed into sparse row format (CSR) and stored. We had analyzed and compared the results of the CA_SAGM, coordinate format (COO) and compressed sparse column format (CSC) algorithms for sparse asymmetric genomic data. Nine types of single-nucleotide variation (SNV) data and six types of copy number variation (CNV) data from the TCGA database were used as the subjects of this study. Compression and decompression time, compression and decompression rate, compression memory and compression ratio were used as evaluation metrics. The correlation between each metric and the basic characteristics of the original data was further investigated. Results: The experimental results showed that the COO method had the shortest compression time, the fastest compression rate and the largest compression ratio, and had the best compression performance. CSC compression performance was the worst, and CA_SAGM compression performance was between the two. When decompressing the data, CA_SAGM performed the best, with the shortest decompression time and the fastest decompression rate. COO decompression performance was the worst. With increasing sparsity, the COO, CSC and CA_SAGM algorithms all exhibited longer compression and decompression times, lower compression and decompression rates, larger compression memory and lower compression ratios. When the sparsity was large, the compression memory and compression ratio of the three algorithms showed no difference characteristics, but the rest of the indexes were still different. Conclusion: CA_SAGM was an efficient compression algorithm that combines compression and decompression performance for sparse genomic mutation data.

Association between ultrasound BI-RADS signs and molecular typing of invasive breast cancer

Objective

To explore the correlation between ultrasound images and molecular typing of invasive breast cancer, so as to analyze the predictive value of preoperative ultrasound for invasive breast cancer.

Methods

302 invasive breast cancer patients were enrolled in Heping Hospital affiliated to Changzhi Medical College in Shanxi, China during 2020 to 2022. All patients accepted ultrasonic and pathological examination, and all pathological tissues received molecular typing with immunohistochemical (IHC) staining. The relevance between different molecular typings and ultrasonic image, pathology were evaluated.

Results

Univariate analysis: among the four molecular typings, there were significant differences in tumor size, shape, margin, lymph node and histological grade (P<0.05). 1. Size: Luminal A tumor was smaller (69.4%), Basal -like type tumors are mostly larger (60.9%); 2. Shape: Basal-like type is more likely to show regular shape (45.7%); 3. Margin: Luminal A and Luminal B mostly are not circumscribed (79.6%, 74.8%), Basal -like type shows circumscribed(52.2%); 4. Lymph nodes: Luminal A type tends to be normal (87.8%), Luminal B type,Her-2+ type and Basal-like type tend to be abnormal (35.6%,36.4% and 39.1%). There was no significant difference in mass orientation, echo pattern, rear echo and calcification (P>0.05). Multivariate analysis: Basal-like breast cancer mostly showed regular shape, circumscribed margin and abnormal lymph nodes (P<0.05).

Conclusion

There are differences in the ultrasound manifestations of different molecular typings of breast cancer, and ultrasound features can be used as a potential imaging index to provide important information for the precise diagnosis and treatment of breast cancer.