PURPOSE: Infarct lesion volume (ILV) may serve as an imaging biomarker for clinical outcomes in the early post-treatment stage in patients with acute ischemic stroke. The aim of this study was to evaluate the inter- and intra-rater reliability of manual segmentation of ILV on follow-up non-contrast CT (NCCT) scans. METHODS: Fifty patients from the Prove-IT study were randomly selected for this analysis. Three raters manually segmented ILV on 24-h NCCT scans, slice by slice, three times. The reference standard for ILV was generated by the Simultaneous Truth And Performance Level estimation (STAPLE) algorithm. Intra- and inter-rater reliability was evaluated, using metrics of intraclass correlation coefficient (ICC) regarding lesion volume and the Dice similarity coefficient (DSC). RESULTS: Median age of the 50 subjects included was 74.5 years (interquartile range [IQR] 67-80), 54% were women, median baseline National Institutes of Health Stroke Scale was 18 (IQR 11-22), median baseline ASPECTS was 9 (IQR 6-10). The mean reference standard ILV was 92.5 ml (standard deviation (SD) ± 100.9 ml). The manually segmented ILV ranged from 88.2 ± 91.5 to 135.5 ± 119.9 ml (means referring to the variation between readers, SD within readers). Inter-rater ICC was 0.83 (95%CI: 0.76-0.88); intra-rater ICC ranged from 0.85 (95%CI: 0.72-0.92) to 0.95 (95%CI: 0.91-0.97). The mean DSC among the three readers ranged from 65.5 ± 22.9 to 76.4 ± 17.1% and the mean overall DSC was 72.8 ± 23.0%. CONCLUSION: Manual ILV measurements on follow-up CT scans are reliable to measure the radiological outcome despite some variability.

Department of Medical Imaging St Anne's University Hospital and Faculty of Medicine Masaryk University Brno Czech Republic

Department of Neurology Medical University of Vienna Vienna Austria

Department of Radiology Cumming School of Medicine University of Calgary Calgary Canada

Department of Radiology University Hospital of Basel Basel Switzerland

Departments of Clinical Neurosciences Calgary Stroke Program Cumming School of Medicine University of Calgary Calgary Canada

Faculty of Medicine in Hradec Kralove Charles University Hradec Kralove Czech Republic

See more in PubMed

Bucker A, Boers AM, Bot JCJ, Berkhemer OA, Lingsma HF, Yoo AJ et al (2017) Associations of ischemic lesion volume with functional outcome in patients with acute ischemic stroke: 24-hour versus 1-week imaging. Stroke 48:1233–1240 DOI

Goyal M, Menon BK, Van Zwam WH, Dippel DWJ, Mitchell PJ, Demchuk AM et al (2016) Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials. Lancet 387:1723–1731 DOI

Hill MD, Goyal M, Menon BK, Nogueira RG, McTaggart RA, Demchuk AM et al (2020) Efficacy and safety of nerinetide for the treatment of acute ischaemic stroke (ESCAPE-NA1): a multicentre, double-blind, randomised controlled trial. Lancet 395:878–887 DOI

Albers GW, Marks MP, Kemp S, Christensen S, Tsai JP, Ortega-Gutierrez S et al (2018) Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging. N Engl J Med 378:708–718 DOI

Nogueira RG, Jadhav AP, Haussen DC, Bonafe A, Budzik RF, Bhuva P et al (2018) Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. N Engl J Med 378:11–21 DOI

Zaidi SF, Aghaebrahim A, Urra X, Jumaa MA, Jankowitz B, Hammer M et al (2012) Final infarct volume is a stronger predictor of outcome than recanalization in patients with proximal middle cerebral artery occlusion treated with endovascular therapy. Stroke 43:3238–3244 DOI

Albers GW, Goyal M, Jahan R, Bonafe A, Diener HC, Levy EI et al (2015) Relationships between imaging assessments and outcomes in solitaire with the intention for thrombectomy as primary endovascular treatment for acute ischemic stroke. Stroke 46:2786–2794 DOI

Kral J, Cabal M, Kasickova L, Havelka J, Jonszta T, Volny O et al (2020) Machine learning volumetry of ischemic brain lesions on CT after thrombectomy—prospective diagnostic accuracy study in ischemic stroke patients. Neuroradiology 62(10):1239–1245 DOI

Cimflova P, Kral J, Volny O, Horn M, Ojha P, Cabal M et al (2021) MRI Diffusion-weighted imaging to measure infarct volume: assessment of manual segmentation variability. J Neuroimaging. https://doi.org/10.1111/jon.12850 PubMed DOI

Baskin A, Buchegger F, Seimbille Y, Ratib O, Garibotto V (2015) PET molecular imaging of hypoxia in ischemic stroke: an update. Curr Vasc Pharmacol 13:209–217 DOI

Heiss WD, Weber OZ (2017) Validation of MRI determination of the penumbra by PET measurements in ischemic stroke. J Nucl Med 58:187–193 DOI

Dohmen C, Bosche B, Graf R, Staub F, Kracht L, Sobesky J et al (2003) Prediction of malignant course in MCA infarction by PET and microdialysis. Stroke 34:2152–2158 DOI

Dohmen C, Galldiks N, Bosche B, Kracht L, Graf R (2012) The severity of ischemia determines and predicts malignant brain edema in patients with large middle cerebral artery infarction. Cerebrovasc Dis 33:1–7 DOI

Goyal M, Ospel JM, Menon B, Almekhlafi M, Jayaraman M, Fiehler J et al (2020) Challenging the ischemic core concept in acute ischemic stroke imaging. Stroke 51:3147–3155 DOI

Warfield SK, Zou KH, Wells WM (2004) Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 23:903–921 DOI

Boers AM, Marquering HA, Jochem JJ, Besselink NJ, Berkhemer OA, Van Der Lugt A et al (2013) Automated cerebral infarct volume measurement in follow-up noncontrast CT scans of patients with acute ischemic stroke. Am J Neuroradiol 34:1522–1527 DOI

Ay H, Arsava EM, Vangel M, Oner B, Zhu M, Wu O et al (2008) Interexaminer difference in infarct volume measurements on MRI: a source of variance in stroke research. Stroke 39:1171–1176 DOI

Luby M, Bykowski JL, Schellinger PD, Merino JG, Warach S (2006) Intra- and interrater reliability of ischemic lesion volume measurements on diffusion-weighted, mean transit time and fluid-attenuated inversion recovery MRI. Stroke 37:2951–2956 DOI

Menon BK, d’Esterre CD, Qazi EM, Almekhlafi M, Hahn L, Demchuk AM et al (2015) Multiphase CT angiography: a new tool for the imaging triage of patients with acute ischemic stroke. Radiology 275:510–520 DOI

Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC et al (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:1116–1128 DOI

Koo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15:155–163 DOI

Kharitonova T, Mikulik R, Roine RO, Soinne L, Ahmed N, Wahlgren N et al (2011) Association of early national institutes of health stroke scale improvement with vessel recanalization and functional outcome after intravenous thrombolysis in ischemic stroke. Stroke 42:1638–1643 DOI

Ospel JM, Jaffray A, Schulze-Zachau V, Kozerke S, Federau C (2020) Spatial resolution and the magnitude of infarct volume measurement error in DWI in acute Ischemic stroke. Am J Neuroradiol 41:792–797 DOI

Straka M, Albers GW, Bammer R (2010) Real-time diffusion-perfusion mismatch analysis in acute stroke. J Magn Reson Imaging 32:1024–1037 DOI

Chen L, Bentley P, Rueckert D (2017) Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. NeuroImage Clin 15:633–643 DOI

Zhang R, Zhao L, Lou W, Abrigo JM, Mok VCT, Chu WCW et al (2018) Automatic segmentation of acute ischemic stroke from DWI using 3-D fully convolutional DenseNets. IEEE Trans Med Imaging 37:2149–2160 DOI

Maier O, Menze BH, von der Gablentz J, Häni L, Heinrich MP, Liebrand M et al (2017) ISLES 2015 - a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med Image Anal 35:250–269 DOI

Tuladhar A, Schimert S, Rajashekar D, Kniep HC, Fiehler J, Forkert ND (2020) Automatic segmentation of stroke lesions in non-contrast computed tomography datasets with convolutional neural networks. IEEE Access 8:94871–94879 DOI

Barros RS, Tolhuisen ML, Boers AMM, Jansen I, Ponomareva E, Dippel DWJ et al (2020) Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks. J Neurointerv Surg 12:848–852 DOI

Fuchigami T, Akahori S, Okatani T, Li Y (2020) A hyperacute stroke segmentation method using 3D U-Net integrated with physicians’ knowledge for NCCT. In: SPIE. SPIE-Intl Soc Optical Eng, p 15. https://doi.org/10.1117/12.2549176

Kuang H, Menon BK, Qiu W (2020) Automated stroke lesion segmentation in non-contrast CT scans using dense multi-path contextual generative adversarial network. Phys Med Biol 65:215013 DOI

Kuang H, Menon BK, Qiu W (2019) Semi-automated infarct segmentation from follow-up noncontrast CT scans in patients with acute ischemic stroke. Med Phys 46:4037–4045 DOI

Kuang H, Najm M, Chakraborty D, Maraj N, Sohn SI, Goyal M et al (2019) Automated aspects on noncontrast CT scans in patients with acute ischemic stroke using machine learning. Am J Neuroradiol 40:33–38 DOI

Pantano P, Caramia F, Bozzao L, Dieler C, Von Kummer R (1999) Delayed increase in infarct volume after cerebral ischemia: correlations with thrombolytic treatment and clinical outcome. Stroke 30:502–507 DOI

Chalela JA, Kasner SE (2000) The fogging effect. Neurology 55:315 DOI

Nicholson C, Syková E (1998) Extracellular space structure revealed by diffusion analysis. Trends Neurosci 21:207–215 DOI

Bosche B, Dohmen C, Graf R, Neveling M, Staub F, Kracht L et al (2003) Extracellular concentrations of non-transmitter amino acids in peri-infarct tissue of patients predict malignant middle cerebral artery infarction. Stroke 34:2908–2913 DOI

Broocks G, Flottmann F, Ernst M, Faizy TD, Minnerup J, Siemonsen S et al (2018) Computed tomography-based imaging of voxel-wise lesion water uptake in ischemic brain: relationship between density and direct volumetry. Invest Radiol 53:207–213 DOI

Broocks G, Leischner H, Hanning U, Flottmann F, Faizy TD, Schön G et al (2020) Lesion age imaging in acute stroke: water uptake in CT versus DWI-FLAIR Mismatch. Ann Neurol 88:1144–1152 DOI