Breast cancer is the most prevalent neoplasia among women, with early and accurate diagnosis critical for effective treatment. In clinical practice, however, the subjective nature of histological grading of infiltrating ductal adenocarcinoma of the breast (DAC-NOS) often leads to inconsistencies among pathologists, posing a significant challenge to achieving optimal patient outcomes. Our study aimed to address this reproducibility problem by leveraging artificial intelligence (AI). We trained a deep-learning model using a convolutional neural network-based algorithm (CNN-bA) on 100 whole slide images (WSIs) of DAC-NOS from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Our model demonstrated high precision, sensitivity, and F1 score across different grading components in about 17.5 h with 19,000 iterations. However, the agreement between the model’s grading and that of general pathologists varied, showing the highest agreement for the mitotic count score. These findings suggest that AI has the potential to enhance the accuracy and reproducibility of breast cancer grading, warranting further refinement and validation of this approach.

COVID-19 has spread in a matter of months to most countries in the world. Various social and economic factors determine the time in which a pandemic reaches a country. This time is essential, because it allows countries to prepare their response. This study considered a gravity model that expressed time to first case as a function of multiple socio-economic factors. First, Kaplan-Meier analysis was performed by comparing upper and lower half values of each variable in the model.In order to measure the effect of these variables on time to first case, parameters of the gravity model were estimated using accelerated failure time (AFT) survival analysis. In the Kaplan-Meier analysis the differences between high and low value groups were significant for every variable except population. The AFT analysis determined that increased personal freedom had the largest effect on lowering the survival time, controlling for detection capacity. Higher GDP per capita and a larger population also reduced survival time, while a greater distance from the outbreak source increased it. Understanding the influence of factors affecting time to index case can help us understand disease spread in the early stages of a pandemic.

Lung squamous cell carcinoma in situ (SCIS) is the pre-invasive precursor lesion of lung squamous cell carcinoma (SCC). Only half of these lesions progress to invasive cancer, while a third undergo spontaneous regression. The ability to predict the evolution of SCIS lesions can significantly impact the management of lung cancer patients. Here, we present the use of the deep learning (DL) approach in order to predict the progression of SCIS. The dataset consisted of 112 H&E stained whole slide images (WSI) that were obtained from the Image Data Resource public repository. The data set corresponded to tumors of patients who underwent biopsies of SCIS lesions and were subsequently followed up by bronchoscopy and CT to monitor for progression to SCC. We show that a deep convolutional neural network (DCNN) can predict if a SCIS lesion will progress to SCC. The model achieved a per-tile AUC of 0.78 (SD = 0.01) on the test set, an F1 score of 0.84 (SD = 0.05), and a sensitivity of 0.94 (SD = 0.01). Class activation maps were created in order to explore how the DCNN made decisions. To our knowledge, this study is the first to demonstrate that DL has the ability to predict the evolution of SCIS from H&E WSI. DL has the potential to be used as a low-cost method that could provide prognostic information for patients with preinvasive lesions.