Prediction of Lymph Node Metastasis in T1 Colorectal Cancer Using Artificial Intelligence with Hematoxylin and Eosin-Stained Whole-Slide-Images of Endoscopic and Surgical Resection Specimens.

According to the current guidelines, additional surgery is performed for endoscopically resected specimens of early colorectal cancer (CRC) with a high risk of lymph node metastasis (LNM). However, the rate of LNM is 2.1-25.0% in cases treated endoscopically followed by surgery, indicating a high rate of unnecessary surgeries. Therefore, this study aimed to develop an artificial intelligence (AI) model using H&E-stained whole slide images (WSIs) without handcrafted features employing surgically and endoscopically resected specimens to predict LNM in T1 CRC. To validate with an independent cohort, we developed a model with four versions comprising various combinations of training and test sets using H&E-stained WSIs from endoscopically (400 patients) and surgically resected specimens (881 patients): Version 1, Train and Test: surgical specimens; Version 2, Train and Test: endoscopic and surgically resected specimens; Version 3, Train: endoscopic and surgical specimens and Test: surgical specimens; Version 4, Train: endoscopic and surgical specimens and Test: endoscopic specimens. The area under the curve (AUC) of the receiver operating characteristic curve was used to determine the accuracy of the AI model for predicting LNM with a 5-fold cross-validation in the training set. Our AI model with H&E-stained WSIs and without annotations showed good performance power with the validation of an independent cohort in a single center. The AUC of our model was 0.758-0.830 in the training set and 0.781-0.824 in the test set, higher than that of previous AI studies with only WSI. Moreover, the AI model with Version 4, which showed the highest sensitivity (92.9%), reduced unnecessary additional surgery by 14.2% more than using the current guidelines (68.3% vs. 82.5%). This revealed the feasibility of using an AI model with only H&E-stained WSIs to predict LNM in T1 CRC.

Transcriptional Changes in Radiation-Induced Lung Injury: A Comparative Analysis of Two Radiation Doses for Preclinical Research.

In a recent stereotactic body radiation therapy animal model, radiation pneumonitis and radiation pulmonary fibrosis were observed at around 2 and 6 weeks, respectively. However, the molecular signature of this model remains unclear. This study aimed to examine the molecular characteristics at these two stages using RNA-seq analysis. Transcriptomic profiling revealed distinct transcriptional patterns for each stage. Inflammatory response and immune cell activation were involved in both stages. Cell cycle processes and response to type II interferons were observed during the inflammation stage. Extracellular matrix organization and immunoglobulin production were noted during the fibrosis stage. To investigate the impact of a 10 Gy difference on fibrosis progression, doses of 45, 55, and 65 Gy were tested. A dose of 65 Gy was selected and compared with 75 Gy. The 65 Gy dose induced inflammation and fibrosis as well as the 75 Gy dose, but with reduced lung damage, fewer inflammatory cells, and decreased collagen deposition, particularly during the inflammation stage. Transcriptomic analysis revealed significant overlap, but differences were observed and clarified in Gene Ontology and KEGG pathway analysis, potentially influenced by changes in interferon-gamma-mediated lipid metabolism. This suggests the suitability of 65 Gy for future preclinical basic and pharmaceutical research connected with radiation-induced lung injury.