UNASSIGNED: This study developed and validated a deep learning (DL) model based on whole slide imaging (WSI) for predicting the treatment response to chemotherapy and radiotherapy (CRT) among patients with non-small cell lung cancer (NSCLC).
UNASSIGNED: We collected the WSI of 120 nonsurgical patients with NSCLC treated with CRT from three hospitals in China. Based on the processed WSI, two DL models were established: a tissue classification model which was used to select tumor-tiles, and another model which predicted the treatment response of the patients based on the tumor-tiles (predicting the treatment response of each tile). A voting method was employed, by which the label of tiles with the greatest quantity from 1 patient would be used as the label of the patient.
UNASSIGNED: The tissue classification model had a great performance (accuracy in the training set/internal validation set =0.966/0.956). Based on 181,875 tumor-tiles selected by the tissue classification model, the model for predicting the treatment response demonstrated strong predictive ability (accuracy of patient-level prediction in the internal validation set/external validation set 1/external validation set 2 =0.786/0.742/0.737).
UNASSIGNED: A DL model was constructed based on WSI to predict the treatment response of patients with NSCLC. This model can help doctors to formulate personalized CRT plans and improve treatment outcomes.

UNASSIGNED: Digital pathology is experiencing an exponential period of growth catalyzed by advancements in imaging hardware and progresses in machine learning. The use of whole slide imaging (WSI) for digital pathology has recently been cleared for primary diagnosis in the US. The demand for using frozen section procedure for rapid identification of cancerous tissue during surgery is another driving force for the development of WSI. A conventional WSI system scans the tissue slide to different positions and acquires the digital images. In a typical implementation, a focus map is created prior to the scanning process, leading to significant overhead time and a necessity for high positional accuracy of the mechanical system. The resulting cost of WSI system is often prohibitive for frozen section procedure during surgery.
UNASSIGNED: We report a novel WSI scheme based on a programmable LED array for sample illumination. In between two regular brightfield image acquisitions, we acquire one additional image by turning on a red and a green LED for color multiplexed illumination. We then identify the translational shift of the red- and green-channel images by maximizing the image mutual information or cross-correlation. The resulting translational shift is used for dynamic focus correction in the scanning process. Since we track the differential focus during adjacent acquisitions, there is no positional repeatability requirement in our scheme.
UNASSIGNED: We demonstrate a prototype WSI platform with a mean focusing error of ~0.3 microns. Different from previous implementations, this prototype platform requires no focus map surveying, no secondary camera or additional optics, and allows for continuous sample motion in the focus tracking process.
UNASSIGNED: A programmable LED array can be used for color-multiplexed single-shot autofocusing in WSI. The reported scheme may enable the development of cost-effective WSI platforms without positional repeatability requirement. It may also provide a turnkey solution for other high-content microscopy applications.