Early diagnosis of breast cancer is crucial for improving survival rates. Radiomics and deep learning (DL) have shown significant potential in assisting radiologists with early cancer detection. This paper aims to critically assess the performance of radiomics, DL, and ensemble techniques in detecting cancer from screening mammograms. Two independent datasets were used: the RSNA 2023 Breast Cancer Detection Challenge (11,913 patients) and a Mexican cohort from the TecSalud dataset (19,400 patients). The ConvNeXtV1-small DL model was trained on the RSNA dataset and validated on the TecSalud dataset, while radiomics models were developed using the TecSalud dataset and validated with a leave-one-year-out approach. The ensemble method consistently combined and calibrated predictions using the same methodology. Results showed that the ensemble approach achieved the highest area under the curve (AUC) of 0.87, compared to 0.83 for ConvNeXtV1small and 0.80 for radiomics. In conclusion, ensemble methods combining DL and radiomics predictions significantly enhance breast cancer diagnosis from mammograms.

Skin cancer cases has increased in the last years and melanoma cases have a high rank in the incidence of mortality. However class imbalance in melanoma dataset produces a challenge for researcher to tackle a classification model. This work proposes the use of a dynamic ratio in dataset imbalance with the addition of synthetic image generation by Progressive Growth of Generative Adversarial Networks (PGGAN) and the assessment of this ratio problem. Our methodology involved the preprocessing of the ISIC 2019 challenge dataset, selection between PGGAN and Wasserstein GAN with Gradient Penalty (WGAN-GP) for image generation, dynamic ratio addition in original dataset, use of Resnet 50 pretrained model and evaluation of classification metrics. Our study demonstrated selected ratios that improved precision, recall and F1-score metrics on melanoma detection against original dataset with traditional image transformations.

Mammographic breast density classification is essential for cancer risk assessment but remains challenging due to subjective interpretation and inter-observer variability. This study compares multimodal and CNN-based methods for automated classification using the BI-RADS system, evaluating BioMedCLIP and ConvNeXt across three learning scenarios: zero-shot classification, linear probing with textual descriptions, and fine-tuning with numerical labels. Results show that zero-shot classification achieved modest performance, while the fine-tuned ConvNeXt model outperformed the BioMedCLIP linear probe. Although linear probing demonstrated potential with pretrained embeddings, it was less effective than full fine-tuning. These findings suggest that despite the promise of multimodal learning, CNN-based models with end-to-end fine-tuning provide stronger performance for specialized medical imaging. The study underscores the need for more detailed textual representations and domain-specific adaptations in future radiology applications.

This study explores a multilingual transfer learning strategy for Named Entity Recognition (NER) in mammography radiology reports, aiming to improve breast cancer diagnosis. By utilizing a dataset from TecSalud, which includes mammograms and Electronic Health Records (EHRs) over ten years, this study seeks to address the linguistic barriers in medical documentation through advanced Natural Language Processing (NLP) models. Our approach involves meticulously labeling twenty-four distinct entities within the predominantly Spanish dataset, covering a range of diagnostic features and interpretive findings, highlighting the challenge of linguistic diversity in medical records and the potential of NLP to bridge this gap.The results demonstrate that fine-tuning on the last layer offers a balanced approach between simplicity and accuracy, avoiding overfitting and achieving state-of-art results.

BACKGROUND: Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the development of de novo areas in imaging science, such as radiomics and radiogenomics, have been adding more to personalize healthcare to stratify patients better. These techniques associate imaging phenotypes with the related disease genes. Various imaging modalities have been used for years to diagnose breast cancer. Nonetheless, digital breast tomosynthesis (DBT), a state-of-the-art technique, has produced promising results comparatively. DBT, a 3D mammography, is replacing conventional 2D mammography rapidly. This technological advancement is key to AI algorithms for accurately interpreting medical images. OBJECTIVE AND METHODS: This paper presents a comprehensive review of deep learning (DL), radiomics and radiogenomics in breast image analysis. This review focuses on DBT, its extracted synthetic mammography (SM), and full-field digital mammography (FFDM). Furthermore, this survey provides systematic knowledge about DL, radiomics, and radiogenomics for beginners and advanced-level researchers. RESULTS: A total of 500 articles were identified, with 30 studies included as the set criteria. Parallel benchmarking of radiomics, radiogenomics, and DL models applied to the DBT images could allow clinicians and researchers alike to have greater awareness as they consider clinical deployment or development of new models. This review provides a comprehensive guide to understanding the current state of early breast cancer detection using DBT images. CONCLUSION: Using this survey, investigators with various backgrounds can easily seek interdisciplinary science and new DL, radiomics, and radiogenomics directions towards DBT.

Alzheimer's disease (AD) is the most common type of dementia and predicting who will convert from Mild Cognitive Impairment (MCI) to AD is crucial to patient benefits as well as medical research. To fulfill this purpose, in recent years it has been reported that the texture of magnetic resonance images can be an effective biomarker. In this study we used images from the Alzheimer's Disease Neuroimaging Initiative database to create T2 maps and identify features related to the texture and signal distribution for the prediction of AD. We extracted 3S features from the left and right hippocampus for 40 patients with MCI who either progressed to AD (18) or remained stable (22) and measured the mean and absolute difference of these contralateral features. We also kept the original volume of each region, yielding a total of 7S features. We used 7 machine learning methods to analyze whether by adding these imaging features to the neuropsychological studies currently used for diagnosis, we could more accurately identify who would develop the disease. We found 11 features significantly different between groups. Furthermore, all but one of the machine learning methods improved their accuracy by adding the signal- and texture-related features, and the volumetric information was non-significant. Our results suggest that these imaging features from hippocampal T2 maps should be further investigated as potential MRI biomarkers for the prediction of AD.

An early diagnosis of Alzheimer's disease (AD) is important for both support and therapeutic planning. Predicting who will progress from mild cognitive impairment (MCI) to AD would yield the same clinical benefits. However, it has been shown that the MCI to AD progression varies depending on certain demographic characteristics. AD is highly associated with the apolipoprotein E type 4 allele expressing the protein isoform APOE4. This study aimed at identifying features associated with the MCI to AD progression whose temporal evolution significantly differs between APOE4 carriers and non-carriers. Longitudinal information from 336 subjects (64.58% carriers) who progressed from MCI to AD was gathered, including laboratory assays, information from MRI and PET analyses, and neuropsychological tests. Longitudinal models identified 11 features with significant differences in their behavior between carriers and non-carriers, demonstrating that the way in which carriers and non-carriers progress from MCI to AD is significantly different.

BACKGROUND: Late-Onset Alzheimer's Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available. RESULTS: We conducted systematic comparisons of representative Machine Learning models for predicting LOAD from genetic variation data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our experimental results demonstrate that the classification performance of the best models tested yielded ∼72% of area under the ROC curve. CONCLUSIONS: Machine learning models are promising alternatives for estimating the genetic risk of LOAD. Systematic machine learning model selection also provides the opportunity to identify new genetic markers potentially associated with the disease.

SUMMARY: The association of genomic alterations to outcomes in cancer is affected by a problem of unbalanced groups generated by the low frequency of alterations. For this, an R package (VALORATE) that estimates the null distribution and the P -value of the log-rank based on a recent reformulation is presented. For a given number of alterations that define the size of survival groups, the log-rank density is estimated by a weighted sum of conditional distributions depending on a co-occurrence term of mutations and events. The estimations are accurately accelerated by sampling across co-occurrences allowing the analysis of large genomic datasets in few minutes. In conclusion, the proposed VALORATE R package is a valuable tool for survival analysis. AVAILABILITY AND IMPLEMENTATION: The R package is available in CRAN at https://cran.r-project.org and in http://bioinformatica.mty.itesm.mx/valorateR . CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Active contour techniques have been widely employed for medical image segmentation. Significant effort has been focused on the use of training data to build prior statistical models applicable specifically to problems where the objects of interest are embedded in cluttered background. Usually, the training data consist of whole shapes of certain organs or structures obtained manually by clinical experts. The resulting prior models enforce segmentation accuracy uniformly over the entire structure or structures to be identified. In this paper, we consider a new coupled prior shape model which is demonstrated to provide high accuracy, specifically in the region of the interest where precision is most needed for the application of the segmentation of the femur and tibia in magnetic resonance (MR) images. Experimental results for the segmentation of MR images of human knees demonstrate that the combination of the new coupled prior shape and a directional edge force provides the improved segmentation performance. Moreover, the new approach allows for equivalent accurate identification of bone marrow lesions, a promising biomarker related to osteoarthritis, to the current state of the art but requires significantly less manual interaction.

UNLABELLED: The analysis of biological networks has become essential to study functional genomic data. Compadre is a tool to estimate pathway/gene sets activity indexes using sub-matrix decompositions for biological networks analyses. The Compadre pipeline also includes one of the direct uses of activity indexes to detect altered gene sets. For this, the gene expression sub-matrix of a gene set is decomposed into components, which are used to test differences between groups of samples. This procedure is performed with and without differentially expressed genes to decrease false calls. During this process, Compadre also performs an over-representation test. Compadre already implements four decomposition methods [principal component analysis (PCA), Isomaps, independent component analysis (ICA) and non-negative matrix factorization (NMF)], six statistical tests (t- and f-test, SAM, Kruskal-Wallis, Welch and Brown-Forsythe), several gene sets (KEGG, BioCarta, Reactome, GO and MsigDB) and can be easily expanded. Our simulation results shown in Supplementary Information suggest that Compadre detects more pathways than over-representation tools like David, Babelomics and Webgestalt and less false positives than PLAGE. The output is composed of results from decomposition and over-representation analyses providing a more complete biological picture. Examples provided in Supplementary Information show the utility, versatility and simplicity of Compadre for analyses of biological networks. AVAILABILITY AND IMPLEMENTATION: Compadre is freely available at http://bioinformatica.mty.itesm.mx:8080/compadre. The R package is also available at https://sourceforge.net/p/compadre.

A novel method for the automated 3D extraction and measurement of soft-tissue lesions in CT imagery is presented. The extraction is carried out using a hybrid algorithm that incorporates elements from both competitive region growth and deformable template techniques. This algorithm is tested against manual tracing and is shown to provide significantly improved performance using three performance metrics: speed, precision and accuracy.