US 12,333,729 B2
Automatic staging of non-small cell lung cancer from medical imaging and biopsy reports
Julian Rosenman, Chapel Hill, NC (US); Zhoubing Xu, Plainsboro, NJ (US); Ali Kamen, Skillman, NJ (US); Fernando Vega, Erlangen (DE); Nicolo Capobianco, Erlangen (DE); Bruce Spottiswoode, Knoxville, TN (US); and Sasa Grbic, Plainsboro, NJ (US)
Assigned to Siemens Medical Solutions USA, Inc., Malvern, PA (US)
Filed by Siemens Medical Solutions USA, Inc., Malvern, PA (US)
Filed on Aug. 11, 2022, as Appl. No. 17/819,256.
Prior Publication US 2024/0054650 A1, Feb. 15, 2024
Int. Cl. G06T 7/00 (2017.01); G16H 10/60 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01)
CPC G06T 7/0016 (2013.01) [G16H 10/60 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/30061 (2013.01); G06T 2207/30096 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving patient data relating to a cancer of a patient, the patient data comprising one or more medical images and one or more biopsy reports;
determining a T-stage of the cancer based on a location and a size of one or more tumors of the cancer determined using the patient data;
determining an N-stage of the cancer by combining a metastasis evaluation of the cancer in regional lymph nodes determined from the one or more medical images and a metastasis evaluation of the cancer in the regional lymph nodes determined from the one or more biopsy reports;
determining an M-stage of the cancer based on a metastasis evaluation of the cancer in anatomical structures based on the patient data; and
outputting the T-stage, the N-stage, and the M-stage,
wherein at least one of the determining the T-stage, the determining of the N-stage, and the determining the M-stage is performed using one or more machine learning based networks.