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Image registration based automated lesion correspondence pipeline for longitudinal CT data by Mukherjee, Subrata; Coroller, Thibaud; Wang, Craig; Samala, Ravi K.; Hu, Tingting; Gokcay, Didem; Petrick, Nicholas; Sahiner, Berkman; Cao, Qian is a scholarly article available to read on EtoBox.

What is Image registration based automated lesion correspondence pipeline for longitudinal CT data about?

Patients diagnosed with metastatic breast cancer (mBC) typically undergo several radiographic assessments during their treatment. mBC often involves multiple metastatic lesions in different organs, it is imperative to accurately track and assess these lesions to gain a comprehensive understanding of the disease's response to treatment. Computerized analysis methods that rely on lesion-level tracking have often used manual matching of corresponding lesions, a time-consuming process that is prone to errors. This paper introduces an automated lesion correspondence algorithm designed to precisely track both targets' lesions and non-targets' lesions in longitudinal data. Here we demonstrate the applicability of our algorithm on the anonymized data from two Phase III trials. The dataset contains imaging data of patients for different follow-up timepoints and the radiologist annotations for the patients enrolled in the trials. Target and non-target lesions are annotated by either one or two groups of radiologists. To facilitate accurate tracking, we have developed a registration-assisted lesion correspondence algorithm. The algorithm employs a sequential two-step pipeline: (a) Firstly, an

Author

Mukherjee, Subrata; Coroller, Thibaud; Wang, Craig; Samala, Ravi K.; Hu, Tingting; Gokcay, Didem; Petrick, Nicholas; Sahiner, Berkman; Cao, Qian

Published

2024

Language

EN