University of Texas System

The State University of New York

Institute for Systems Biology

Rebecca Batiste

Ashish Sharma

Joel Haskin Saltz

Ilya Shmulevich

Tahsin M Kurc

Tianhao Zhao

Arvind Rao

John Van Arnam

Le Hou

Pankaj Singh

Rajarsi Gupta

Vu Nguyen

Alexander Lazar

Dimitrios Samaras

Vesteinn Thorsson

A system associated with quantifying a density level of tumor-infiltrating lymphocytes, based on prediction of reconstructed TIL information associated with tumoral tissue image data during pathology analysis of the tissue image data is disclosed. The system receives digitized diagnostic and stained whole-slide image data related to tissue of a particular type of tumoral data. Defined are regions of interest that represents a portion of, or a full image of the whole-slide image data. The image data is encoded into segmented data portions based on convolutional autoencoding of objects associated with the collection of image data. The density of tumor-infiltrating lymphocytes is determined of bounded segmented data portions for respective classification of the regions of interest. A classification label is assigned to the regions of interest. It is determined whether an assigned classification label is above a pre-determined threshold probability value of lymphocyte infiltrated. The threshold probability value is adjusted in order to re-assign the classification label to the regions of interest based on a varied sensitivity level of density of lymphocyte infiltrated. A trained classification model is generated based on the re-assigned classification labels to the regions of interest associated with segmented data portions using the adjusted threshold probability value. An unlabeled image data set is received to iteratively classify the segmented data portions based on a lymphocyte density level associated with portions of the unlabeled image data set, using the trained classification model. Tumor-infiltrating lymphocyte representations are generated based on prediction of TIL information associated with classified segmented data portions. A refined TIL representation based on prediction of the TIL representations is generated using the adjusted threshold probability value associated with the classified segmented data portions. A corresponding method and computer-readable device are also disclosed.

System and method to quantify tumor-infiltrating lymphocytes (TILs) for clinical pathology analysis based on prediction, spatial analysis, molecular correlation, and reconstruction of TIL information identified in digitized tissue images

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Stony Brook, NY, United States of America

Rebecca Batiste