Automated Registration, Segmentation, and Measurement of Metastatic Melanoma Tumors in Serial CT Scans

Abstract

Our goal was to evaluate a new software capability that integrates registration, segmentation and tumor measurement across serial exams within a picture archiving communication system (PACS) to expedite tumor measurement. Patients treated under institutional review board-approved protocols for metastatic melanoma were retrospectively reviewed. Of the 19 included patients, five were male, the median age was 43.2, and all received treatment using an adoptive cell therapy. Seventy-one lung, liver, and subcutaneous tumors were manually measured using RECIST (Response Evaluation Criteria In Solid Tumors) criteria before therapy (baseline computed tomography [CT]) and within 3 months after therapy (follow-up CT). We performed semiautomated registration, segmentation, and RECIST measurements at both time points within PACS (Carestream Health, Rochester, NY). We compared manual and software-generated RECIST measurements using Bland-Altman plots. The median manually measured RECIST diameter for all baseline tumors was 2.1 (1.0-6.2) cm. The refined registration function identified 70/71 (98.6%) tumors on the follow-up CT. On the baseline CT, all 21 liver, 27/32 (84%) lung, and 10/18 (55%) subcutaneous tumors completed segmentation. On the follow-up CT, 19/21 (90%) liver, 21/27 (78%) lung, and 8/10 (80%) subcutaneous tumors completed segmentation. The Bland-Altman plot demonstrated a 95% confidence interval of ±0.7 cm when comparing the software-generated and manual RECIST measurements. The PACS software performed semiautomated baseline tumor measurements and fully automated follow-up tumor measurements in a majority of lung, liver, and subcutaneous tumors. In our patients, semiautomated metastatic tumor measurement did not obviate the need for physician oversight due to disease and treatment-related factors.