|
Title
|
|
|
|
Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases
| |
|
Author
|
|
|
|
| |
|
Abstract
|
|
|
|
| Background We developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metastases (CRLM). Methods In this prospective cohort study, pre- and post-systemic treatment computed tomography (CT) scans of 259 patients with initially unresectable CRLM of the CAIRO5 trial (NCT02162563) were included. In total, 595 CT scans comprising 8,959 CRLM were divided into training (73%), validation (6.5%), and test sets (21%). Deep learning models were trained with ground truth segmentations of the liver and CRLM. TTV was calculated based on the CRLM segmentations. An external validation cohort was included, comprising 72 preoperative CT scans of patients with 112 resectable CRLM. Image segmentation evaluation metrics and intraclass correlation coefficient (ICC) were calculated. Results In the test set (122 CT scans), the autosegmentation models showed a global Dice similarity coefficient (DSC) of 0.96 (liver) and 0.86 (CRLM). The corresponding median per-case DSC was 0.96 (interquartile range [IQR] 0.95-0.96) and 0.80 (IQR 0.67-0.87). For tumor segmentation, the intersection-over-union, precision, and recall were 0.75, 0.89, and 0.84, respectively. An excellent agreement was observed between the reference and automatically computed TTV for the test set (ICC 0.98) and external validation cohort (ICC 0.98). In the external validation, the global DSC was 0.82 and the median per-case DSC was 0.60 (IQR 0.29-0.76) for tumor segmentation. Conclusions Deep learning autosegmentation models were able to segment the liver and CRLM automatically and accurately in patients with initially unresectable CRLM, enabling automatic TTV assessment in such patients. Relevance statement Automatic segmentation enables the assessment of total tumor volume in patients with colorectal liver metastases, with a high potential of decreasing radiologist's workload and increasing accuracy and consistency. Key points center dot Tumor response evaluation is time-consuming, manually performed, and ignores total tumor volume. center dot Automatic models can accurately segment tumors in patients with colorectal liver metastases. center dot Total tumor volume can be accurately calculated based on automatic segmentations. |
| |
|
Language
|
|
|
|
English
| |
|
Source (journal)
|
|
|
|
European Radiology Experimental / European Society of Radiology (ESR)
| |
|
Publication
|
|
|
|
SpringerOpen
,
2023
| |
|
ISSN
|
|
|
|
2509-9280
| |
|
DOI
|
|
|
|
10.1186/S41747-023-00383-4
| |
|
Volume/pages
|
|
|
|
7
:1
(2023)
, p. 1-13
| |
|
Article Reference
|
|
|
|
75
| |
|
ISI
|
|
|
|
001111028000001
| |
|
Pubmed ID
|
|
|
|
38038829
| |
|
Medium
|
|
|
|
E-only publicatie
| |
|
Full text (Publisher's DOI)
|
|
|
|
| |
|
Full text (open access)
|
|
|
|
| |
|