Follow-up of liver metastases: a comparison of deep learning and RECIST 1.1.
Follow-up studies
Liver metastases
Response evaluation criteria in solid tumors
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
07
01
2023
accepted:
14
05
2023
revised:
25
04
2023
medline:
27
11
2023
pubmed:
22
7
2023
entrez:
22
7
2023
Statut:
ppublish
Résumé
To compare liver metastases changes in CT assessed by radiologists using RECIST 1.1 and with aided simultaneous deep learning-based volumetric lesion changes analysis. A total of 86 abdominal CT studies from 43 patients (prior and current scans) of abdominal CT scans of patients with 1041 liver metastases (mean = 12.1, std = 11.9, range 1-49) were analyzed. Two radiologists performed readings of all pairs; conventional with RECIST 1.1 and with computer-aided assessment. For computer-aided reading, we used a novel simultaneous multi-channel 3D R2U-Net classifier trained and validated on other scans. The reference was established by having an expert radiologist validate the computed lesion detection and segmentation. The results were then verified and modified as needed by another independent radiologist. The primary outcome measure was the disease status assessment with the conventional and the computer-aided readings with respect to the reference. For conventional and computer-aided reading, there was a difference in disease status classification in 40 out of 86 (46.51%) and 10 out of 86 (27.9%) CT studies with respect to the reference, respectively. Computer-aided reading improved conventional reading in 30 CT studies by 34.5% for two readers (23.2% and 46.51%) with respect to the reference standard. The main reason for the difference between the two readings was lesion volume differences (p = 0.01). AI-based computer-aided analysis of liver metastases may improve the accuracy of the evaluation of neoplastic liver disease status. AI may aid radiologists to improve the accuracy of evaluating changes over time in metastasis of the liver. • Classification of liver metastasis changes improved significantly in one-third of the cases with an automatically generated comprehensive lesion and lesion changes report. • Simultaneous deep learning changes detection and volumetric assessment may improve the evaluation of liver metastases temporal changes potentially improving disease management.
Identifiants
pubmed: 37480549
doi: 10.1007/s00330-023-09926-0
pii: 10.1007/s00330-023-09926-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
9320-9327Informations de copyright
© 2023. The Author(s), under exclusive licence to European Society of Radiology.
Références
Eisenhauer EA, Therasse P (2009) Bogaerts J, et al New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45(2):228–247
doi: 10.1016/j.ejca.2008.10.026
pubmed: 19097774
Litière S, Isaac G et al (2019) On behalf of the RECIST Working Group. RECIST 1.1 for response evaluation apply not only to chemotherapy-treated patients but also to targeted cancer agents: a pooled database analysis. J Clin Oncol 37(13):1102–1110
doi: 10.1200/JCO.18.01100
pubmed: 30860949
pmcid: 6494357
Kuhl CK, Alparslan Y, Schmoee J et al (2019) Validity of RECIST Version 1.1 for response assessment in metastatic cancer: a prospective, multireader study. Radiology 290(2):349–356
doi: 10.1148/radiol.2018180648
pubmed: 30398433
Sosna J (2019) Is RECIST Version 1.1 reliable for tumor response assessment in metastatic cancer? Radiology 290(2):357–358
doi: 10.1148/radiol.2018182179
pubmed: 30398437
Gonzalez-Guindalini FD, Botelho MP (2013) Harmath CB et al Assessment of liver tumor response to therapy: role of quantitative imaging. Radiograph 33(6):1781–1800
doi: 10.1148/rg.336135511
Winter KS, Hofmann FO (2018) Thierfelder KM et al Towards volumetric thresholds in RECIST 1.1: therapeutic response assessment in hepatic metastases. Eur Radiol 28(11):4839–4848
doi: 10.1007/s00330-018-5424-0
pubmed: 29736851
Moreau N, Rousseau C (2021) Fourcade C et al Automatic segmentation of metastatic breast cancer lesions on
doi: 10.3390/cancers14010101
Li W, Jia F, Hu Q (2015) Automatic segmentation of liver tumors in CT images with deep convolutional neural networks. J Comput Commun 3(11):146
doi: 10.4236/jcc.2015.311023
Ben-Cohen A, Diamant I, Klang E, Amitai M, Greenspan H (2016) Fully convolutional network for liver segmentation and lesions detection. In: et al. Deep Learning and Data Labeling for Medical Applications. DLMIA LABELS 2016 2016. Lecture Notes in Computer Science, vol 10008. Springer, Cham. https://doi.org/10.1007/978-3-319-46976-8_9
Vorontsov E, Cerny M (2019) Régnier P et al Deep learning for automated segmentation of liver lesions at CT in patients with colorectal cancer liver metastases. Radiol Artif Intell 1(2):180014
doi: 10.1148/ryai.2019180014
pubmed: 33937787
pmcid: 8017429
Kim K, Kim S, Han K, Bae H, Shin J, Seok LJ (2021) Diagnostic performance of deep learning-based lesion detection algorithm in CT for detecting hepatic metastasis from colorectal cancer. Korean J Radiol 22(6):912–921
doi: 10.3348/kjr.2020.0447
pubmed: 33686820
pmcid: 8154788
Vivanti R, Szeskin A, Lev-Cohain N, Sosna J, Joskowicz L (2017) Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies. Int J Comput Assist Radiol Surg 12(11):1945–1957
doi: 10.1007/s11548-017-1660-z
pubmed: 28856515
Vivanti R, Joskowicz L, Ephrat A, Lev-Cohain N, Sosna J (2018) Patient-specific convolutional neural networks for robust automatic liver tumor delineation in longitudinal CT studies. Med Biol Eng Comput 56(9):1699–1713
doi: 10.1007/s11517-018-1803-6
pubmed: 29524116
Szeskin A, Rochman S, Weis S, Lederman R, Sosna J, Joskowicz L (2023) Liver lesion changes analysis in longitudinal CECT scans by simultaneous deep learning voxel classification with SimUNet. Med Image Anal 83:102675
Alom Z, Hasan M, Yakopcic C, Taha TM, Asari VK (2019) Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation. J Med Imaging (Bellingham) 6(1):014006
pubmed: 30944843
Shafiei A, Bagheri M, Farhadi F et al (2021) CT Evaluation of lymph nodes that merge or split during the course of a clinical trial: limitations of RECIST 1.1. Radiol Imaging Cancer 3(3):e200090 PMID: 33874734
doi: 10.1148/rycan.2021200090
pubmed: 33874734
pmcid: 8189184