US 12,254,710 B2
Predicting cancer recurrence from spatial multi-parameter cellular and subcellular imaging data
Srinivas C. Chennubhotla, Pittsburgh, PA (US); Douglass L. Taylor, Pittsburgh, PA (US); and Shikhar Uttam Fnu, Pittsburgh, PA (US)
Assigned to University of Pittsburgh—Of The Commonwealth System of Higher Education, Pittsburgh, PA (US)
Filed by UNIVERSITY OF PITTSBURGH-OF THE COMMONWEALTH SYSTEM OF HIGHER EDUCATION, Pittsburgh, PA (US)
Filed on Oct. 24, 2023, as Appl. No. 18/493,740.
Application 18/493,740 is a continuation of application No. 17/056,080, granted, now 11,836,998, previously published as PCT/US2019/033662, filed on May 23, 2019.
Claims priority of provisional application 62/675,832, filed on May 24, 2018.
Prior Publication US 2024/0062564 A1, Feb. 22, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06V 20/69 (2022.01); G06F 18/21 (2023.01); G06F 18/211 (2023.01); G06V 10/26 (2022.01); G06V 10/50 (2022.01); G06V 10/771 (2022.01); G06V 10/776 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06N 20/00 (2019.01)
CPC G06V 20/698 (2022.01) [G06F 18/211 (2023.01); G06F 18/217 (2023.01); G06V 10/26 (2022.01); G06V 10/50 (2022.01); G06V 10/771 (2022.01); G06V 10/776 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06N 20/00 (2019.01); G06V 2201/03 (2022.01)] 22 Claims
OG exemplary drawing
 
1. A method of predicting cancer recurrence risk for an individual, comprising:
receiving patient spatial multi-parameter cellular and sub-cellular imaging data labelled with a plurality of different biomarkers for a tumor of the individual;
analyzing the patient spatial multi-parameter cellular and sub-cellular imaging data using a joint prognostic model for predicting cancer recurrence risk to determine a predicted cancer recurrence risk for the individual, wherein the joint prognostic model has been previously developed and trained by:
receiving spatial multi-parameter cellular and sub-cellular imaging data labelled with the plurality of different biomarkers for a plurality of cancer patients, wherein the spatial multi-parameter cellular and sub-cellular imaging data for the plurality of cancer patients comprises multiplexed immunofluorescence biomarker data;
performing a spatial dissection of the spatial multi-parameter cellular and sub-cellular imaging data for the plurality of cancer patients including cell segmentation to divide the multiplexed immunofluorescence biomarker data into a plurality of intra-tumor spatial domains;
generating a base feature set for each of the intra-tumor spatial domains, wherein for each intra-tumor spatial domain the base feature set includes: (i) a computed intensity expression value for each of the plurality of different biomarkers, wherein the computed intensity expression value for each biomarker is a mean intensity value for the biomarker averaged across all cells within the intra-tumor spatial domain expressing the biomarker, and (ii) a plurality of Kendall rank correlation values, wherein each Kendall rank correlation value is between a respective pair of biomarkers of the plurality of different biomarkers for all cells within the intra-tumor spatial domain expressing the respective pair of biomarkers;
for each of the intra-tumor spatial domains, determining an optimal subset of features from the base feature set for the intra-tumor spatial domain by testing each feature of the base feature set of the intra-tumor spatial domain using a regression method and determining those specific features from the base feature set that constitute the optimal subset;
for each of the intra-tumor spatial domains, developing and training a spatial domain specific multivariate prognostic model for predicting cancer recurrence risk using the optimal subset of features of the intra-tumor spatial domain; and
combining the spatial domain specific multivariate prognostic model of each of the intra-tumor spatial domains to form the joint prognostic model for predicting cancer recurrence risk.