{ "id": "ep3z9fasec", "slug": "ct-severity-score", "title": "CT severity score", "updated": "2026-10-15", "topic_path": [ { "id": "life", "label": "Life and health", "api_url": "https://www.edgepedia.org/api/v1/topics/life" }, { "id": "life.health", "label": "Human health and medicine", "api_url": "https://www.edgepedia.org/api/v1/topics/life.health" }, { "id": "life.health.clinical", "label": "Clinical assessment and procedures", "api_url": "https://www.edgepedia.org/api/v1/topics/life.health.clinical" }, { "id": "life.health.clinical.clinical-imaging", "label": "Medical imaging and radiography", "api_url": "https://www.edgepedia.org/api/v1/topics/life.health.clinical.clinical-imaging" }, { "id": "life.health.clinical.clinical-imaging.image-analysis-and-quantitative-imaging", "label": "Image analysis and quantitative imaging", "api_url": "https://www.edgepedia.org/api/v1/topics/life.health.clinical.clinical-imaging.image-analysis-and-quantitative-imaging" } ], "geo": [ { "id": "geo.nongeo.t2001.life.health.clinical.clinical-imaging.image-analysis-and-quantitative-imaging", "label": "Non-geographic · 2001 to 2020: Image analysis and quantitative imaging", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.health.clinical.clinical-imaging.image-analysis-and-quantitative-imaging", "path": [ { "id": "geo.nongeo", "label": "Non-geographic", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo" }, { "id": "geo.nongeo.t2001", "label": "Non-geographic · 2001 to 2020", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001" }, { "id": "geo.nongeo.t2001.life", "label": "Life and health", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life" }, { "id": "geo.nongeo.t2001.life.health", "label": "Human health and medicine", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.health" }, { "id": "geo.nongeo.t2001.life.health.clinical", "label": "Clinical assessment and procedures", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.health.clinical" }, { "id": "geo.nongeo.t2001.life.health.clinical.clinical-imaging", "label": "Medical imaging and radiography", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.health.clinical.clinical-imaging" }, { "id": "geo.nongeo.t2001.life.health.clinical.clinical-imaging.image-analysis-and-quantitative-imaging", "label": "Image analysis and quantitative imaging", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.health.clinical.clinical-imaging.image-analysis-and-quantitative-imaging" } ] } ], "excerpt": "The CT severity score (CT-SS) is a semi-quantitative radiological method grading lung involvement on chest CT, widely applied during the COVID-19 pandemic to predict ICU admission and death.", "snippet": "The CT severity score (CT-SS) is a semi-quantitative radiological method grading lung involvement on chest CT, widely applied during the COVID-19 pandemic to predict ICU admission and death.", "node": "life.health.clinical.clinical-imaging.image-analysis-and-quantitative-imaging", "markdown": "# CT severity score\n\nThe CT severity score (CT-SS) is a semi-quantitative radiological method for grading the extent of lung involvement on chest CT, in which the lungs are divided into defined regions, each region is assigned a small ordinal score for the percentage of parenchymal opacification, and the region scores are summed into a single number. It became one of the most widely applied imaging severity measures during the COVID-19 pandemic, where it was used to estimate disease severity and predict outcomes such as intensive care unit (ICU) admission and death.[1](https://doi.org/10.1148/ryct.2020200047) • [2](https://doi.org/10.1186/s43055-021-00525-x) The method is visual rather than volumetric: the reader estimates involvement by eye against percentage thresholds, which makes it fast.[3](https://www.nature.com/articles/s41598-021-84561-7)\n\n| Key fact | Detail |\n|---|---|\n| Original CT-SS output | 20 lung segment regions scored 0–2 each, total range 0–40 points[1](https://doi.org/10.1148/ryct.2020200047) |\n| 25-point lobar variant | Five lobes scored 0–5 (0%, <5%, 5–25%, 26–49%, 50–75%, >75% involvement), total 0–25[2](https://doi.org/10.1186/s43055-021-00525-x) |\n| Total severity score (TSS) | Five lobes scored 0–4 (0%, 1–25%, 26–50%, 51–75%, 76–100%), total 0–20[2](https://doi.org/10.1186/s43055-021-00525-x) |\n| Pooled accuracy for severity | Sensitivity 0.85, specificity 0.86, summary AUC 0.91 across 17 studies (2788 patients)[4](https://pmc.ncbi.nlm.nih.gov/articles/PMC9933858/) |\n| Pooled accuracy for mortality | Sensitivity 0.77, specificity 0.79, summary AUC 0.84 across 6 studies (1403 patients)[4](https://pmc.ncbi.nlm.nih.gov/articles/PMC9933858/) |\n| Reported cutoffs (one cohort) | CTSS > 18.5 predicted ICU admission; CTSS > 19.5 predicted increased mortality[5](https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2023.1125530/full) |\n| Inter-rater reliability | Weighted Cohen's κ 0.71–0.86 between three human readers of the 25-point Pan-score[6](https://www.mdpi.com/2075-4418/15/16/1987) |\n\n## How it works\n\nAll CT severity scores rest on the same principle: the lung parenchyma is partitioned into anatomically defined units, the reader visually estimates what fraction of each unit shows abnormal opacification, and that fraction is converted to a small ordinal grade through fixed percentage thresholds. The findings being graded are the typical appearances of viral pneumonia, principally [ground-glass opacity](https://www.edgepedia.org/ground-glass-opacity) and consolidation; one modified system additionally records the qualitative character of involvement as ground-glass opacity (A), crazy-paving pattern (B), consolidation (C), or other findings (X).[7](https://doi.org/10.1007/s00330-021-08432-5) Summing the regional grades yields a total in which higher numbers mean a larger proportion of aerated lung has been lost to disease.\n\nThe partitioning differs between systems. The original CT-SS divides the 18 anatomic segments of both lungs into 20 regions and scores each 0, 1, or 2 for 0%, less than 50%, or 50% or more opacification, for a total of 0 to 40.[1](https://doi.org/10.1148/ryct.2020200047) • [7](https://doi.org/10.1007/s00330-021-08432-5) Lobar systems use the five lobes with finer percentage bands, and zone- or field-based systems use coarser divisions.\n\n## How it is done\n\nThe reader works through the following steps for a given chest CT examination:\n\n1. Review the axial images.[6](https://www.mdpi.com/2075-4418/15/16/1987)\n2. For each defined unit (20 segment regions in the original CT-SS, or the five lobes in lobar variants), estimate the percentage of the unit showing parenchymal opacification.[1](https://doi.org/10.1148/ryct.2020200047)\n3. Convert each estimate to the scheme's grade: 0–2 per region for the CT-SS[1](https://doi.org/10.1148/ryct.2020200047); 0–5 per lobe (0%, <5%, 5–25%, 26–49%, 50–75%, >75%) for the 25-point score[2](https://doi.org/10.1186/s43055-021-00525-x); 0–4 per lobe (0%, 1–25%, 26–50%, 51–75%, 76–100%) for the TSS.[2](https://doi.org/10.1186/s43055-021-00525-x)\n4. Sum the grades to the total score (0–40, 0–25, or 0–20 respectively) and interpret it against the scheme's severity bands or validated cutoffs.[1](https://doi.org/10.1148/ryct.2020200047) • [5](https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2023.1125530/full)\n\n## Origin\n\nThe 40-point, 20-region CT severity score was introduced by Ran Yang and colleagues in \"Chest CT Severity Score: An Imaging Tool for Assessing Severe COVID-19\", published in Radiology Cardiothoracic Imaging in 2020.[1](https://doi.org/10.1148/ryct.2020200047) Several competing semi-quantitative proposals appeared in the same early pandemic period, and published comparisons disagree about who proposed which lobar scheme. One comparison attributes the 25-point five-lobe score to Kunhua Li and colleagues and calls it the most widely used, while another body of literature credits a 2005 SARS-era score as its basis.[2](https://doi.org/10.1186/s43055-021-00525-x) • [8](https://qims.amegroups.org/article/view/101192/html) What is consistent across sources is that the 25-point lobar scheme (known in much of the literature as the Pan score) became the most established visual CT scoring system worldwide during the pandemic.[9](https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1578282/full)\n\n## Variants\n\nThe variants differ mainly in how the lungs are divided and how involvement is weighted:\n\n- Segmental 20-region systems score each region 0–2 for a 0–40 total; a closely related 18-segment version scores 0–2 per segment for a 0–36 total.[1](https://doi.org/10.1148/ryct.2020200047) • [3](https://www.nature.com/articles/s41598-021-84561-7)\n- Lobar systems include the 0–25 score (five lobes, 0–5 each)[2](https://doi.org/10.1186/s43055-021-00525-x), the TSS (0–20, five lobes, 0–4 each)[2](https://doi.org/10.1186/s43055-021-00525-x), and a 0–15 visual score grading each lobe 0–3 based on ground-glass opacity and consolidation.[10](https://ajronline.org/doi/10.2214/AJR.20.24044)\n- Weighted and multi-level systems include an opacity-weighted segmental assessment that multiplies extent by opacity grade for a 0–72 total[3](https://www.nature.com/articles/s41598-021-84561-7), a CTSS-Yuan system scoring six lung zones on a 3-point radiologic scale multiplied by a 4-point extent scale (0–72)[5](https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2023.1125530/full), and a 3-level chest severity score that multiplies extent (0–4) by nature (1–4) at three anatomic levels per lung for a 0–96 range.[7](https://doi.org/10.1007/s00330-021-08432-5)\n- Modified and combined systems include the modified TSS, which appends the qualitative letters A/B/C/X to the 0–20 score[7](https://doi.org/10.1007/s00330-021-08432-5), a combined morphologic/volumetric 25-point CTSI adding eight morphological patterns to four volumetric grades[11](https://doi.org/10.1186/s43055-021-00486-1), a simplified whole-lung 0–5 score[2](https://doi.org/10.1186/s43055-021-00525-x), and the lung field-based severity score (LFSS), which scores six lung fields 0–4 by opacification percentage.[12](https://doi.org/10.21037/jtd-24-544)\n- Post-pandemic systems include the multi-dimensional RACOON Viral Pneumonia Score (RVPS), which scores five finding categories 0–5 by volume involvement across four sections and ranges 0 to 52.[9](https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1578282/full)\n\n## Applications\n\nA systematic review and meta-analysis pooled CTSS diagnostic accuracy across 17 studies (2788 patients) for disease severity, giving sensitivity 0.85 (95% CI 0.78–0.90), specificity 0.86 (95% CI 0.76–0.92), and summary AUC 0.91 (95% CI 0.89–0.94); for mortality across 6 studies (1403 patients), pooled sensitivity was 0.77 (95% CI 0.69–0.83), specificity 0.79 (95% CI 0.72–0.85), and sAUC 0.84 (95% CI 0.81–0.87).[4](https://pmc.ncbi.nlm.nih.gov/articles/PMC9933858/)\n\nIn individual cohorts, higher scores tracked worse outcomes. In 227 COVID-19 patients, higher CTSS was associated with ICU admission, ventilation, and death (all \\( p < 0.001 \\)), with cutoffs of 18.5 for ICU admission and 19.5 for increased mortality at roughly 60–70% sensitivity and specificity.[5](https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2023.1125530/full) In another cohort, a CT score ≥ 18 remained an independent predictor of death in multivariate analysis (HR 3.74; 95% CI 1.10–12.77).[13](https://pubmed.ncbi.nlm.nih.gov/32623505/) CTSS also correlates with clinical pneumonia severity scores ([CURB-65](https://www.edgepedia.org/curb-65), PSI/PORT) and inflammatory markers including CRP, ferritin, and the neutrophil/lymphocyte ratio.[4](https://pmc.ncbi.nlm.nih.gov/articles/PMC9933858/) The simplified whole-lung 0–5 score matched the 25-point lobar score for validity (AUC 0.89 vs 0.90) with higher interreader agreement.[2](https://doi.org/10.1186/s43055-021-00525-x)\n\nBeyond COVID-19, the Pan score was not developed for, nor convincingly validated in, pneumonias from pathogens other than [SARS-CoV-2](https://www.edgepedia.org/sars-cov-2), which motivated the broader RVPS for monitoring infectious lung disease from the acute stage to post-pneumonic sequelae.[9](https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1578282/full) Deep-learning pipelines can now estimate total severity scores directly: a 3D-UNet integrated with DenseNet169 segmented lung lobes and lesions, and its calculated TSS matched radiologist evaluations with an \\( R^{2} \\) of 0.842.[14](https://www.nature.com/articles/s41598-023-47743-z)\n\n## Limitations and alternatives\n\nSemi-quantitative visual scoring has a ceiling effect: at high disease burden the coarse percentage bands saturate, which can bias results in the most severe patients.[3](https://www.nature.com/articles/s41598-021-84561-7) Quantitative lesion-volume measurement from segmented CT trades this against different errors: it achieves higher specificity but lower sensitivity than semi-quantitative scoring for distinguishing disease severity, and manual segmentation is time-consuming.[3](https://www.nature.com/articles/s41598-021-84561-7) A comparison across schemes found the lobar 0–25 score a reasonable default because of its sensitivity for severe disease, low time cost, and clinical availability.[3](https://www.nature.com/articles/s41598-021-84561-7)\n\nPerformance degrades in the sickest patients. In a prospective ICU cohort of 226 critically ill COVID-19 patients, higher admission CTSS predicted ventilation duration and ventilator-associated pneumonia but showed no association with in-hospital mortality; the authors attributed this partly to the lack of score standardization across studies and to most validation populations being non-ICU patients.[15](https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0299390) External validation in 566 hospitalized patients found the 15-, 20-, and 24-point systems all distinguished survivors from nonsurvivors (P ≤ .001) but with modest discrimination (AUC 0.646–0.672), well below the pooled estimates from broader cohorts, and concluded that the five-lobe 0–20 scheme is more effective for predicting mortality.[16](https://link.springer.com/article/10.1186/s43055-025-01622-x) [Reproducibility](https://www.edgepedia.org/reproducibility) is generally good: in a 2025 study of 569 PCR-confirmed patients, the 25-point Pan-score applied by three human readers of different training levels and by dedicated AI-based chest CT software showed high inter-rater agreement (weighted Cohen's κ 0.71–0.86 between humans, 0.83 between humans and AI), although the AI mean score ran significantly higher (\\( 10.44 \\pm 5.10 \\) vs \\( 9.35 \\pm 6.03 \\), \\( p < 0.001 \\)).[6](https://www.mdpi.com/2075-4418/15/16/1987) Published head-to-head comparisons with CO-RADS, the Brixia score, or the WHO ordinal scale are lacking.\n\n## References\n\n1. [Ran Yang and colleagues (2020). Chest CT Severity Score: An Imaging Tool for Assessing Severe COVID-19. Radiology Cardiothoracic Imaging.](https://doi.org/10.1148/ryct.2020200047)\n2. [Mohamed Abdel-Tawab and colleagues (2021). A simple chest CT score for assessing the severity of pulmonary involvement in COVID-19. The Egyptian Journal of Radiology and Nuclear Medicine.](https://doi.org/10.1186/s43055-021-00525-x)\n3. [Quantitative and semi-quantitative CT assessments of lung lesion burden in COVID-19 pneumonia (Scientific Reports)](https://www.nature.com/articles/s41598-021-84561-7)\n4. [Computed tomography severity score as a predictor of disease severity and mortality in COVID-19 patients: A systematic review and meta-analysis](https://pmc.ncbi.nlm.nih.gov/articles/PMC9933858/)\n5. [The importance of chest CT severity score and lung CT patterns in risk assessment in COVID-19-associated pneumonia: a comparative study (Frontiers in Medicine, 2023)](https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2023.1125530/full)\n6. [Visual and AI-Based Assessment of COVID-19 Pneumonia: Practicability and Reproducibility of an Established Semi-Quantitative Chest CT Scoring System (Diagnostics, 2025)](https://www.mdpi.com/2075-4418/15/16/1987)\n7. [Ali H. Elmokadem and colleagues (2022). Comparison of chest CT severity scoring systems for COVID-19. European Radiology.](https://doi.org/10.1007/s00330-021-08432-5)\n8. [Assessment of COVID-19 lung involvement on computed tomography by deep-learning-, threshold-, and human reader-based approaches, an international, multi-center comparative study (Quantitative Imaging in Medicine and Surgery)](https://qims.amegroups.org/article/view/101192/html)\n9. [The RACOON viral pneumonia score for structured reporting of pre-existing, acute, and post-pneumonic findings on chest CT (Frontiers in Medicine, 2025)](https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1578282/full)\n10. [American Journal of Roentgenology, CT visual severity score prognostic study](https://ajronline.org/doi/10.2214/AJR.20.24044)\n11. [Ahmed Samir and colleagues (2021). COVID-19 clinico-radiological mismatch: a proposal for a novel combined morphologic/volumetric CT severity score with blinded validation. The Egyptian Journal of Radiology and Nuclear Medicine.](https://doi.org/10.1186/s43055-021-00486-1)\n12. [Xin’ang Jiang and colleagues (2024). Lung field-based severity score (LFSS): a feasible tool to identify COVID-19 patients at high risk of progressing to critical disease. Journal of Thoracic Disease.](https://doi.org/10.21037/jtd-24-544)\n13. [Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis (PubMed record)](https://pubmed.ncbi.nlm.nih.gov/32623505/)\n14. [Segmentation of lung lobes and lesions in chest CT for the classification of COVID-19 severity (Scientific Reports, 2023)](https://www.nature.com/articles/s41598-023-47743-z)\n15. [Association of chest computed tomography severity score at ICU admission and respiratory outcomes in critically ill COVID-19 patients (PLOS One)](https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0299390)\n16. [External validation of CT-based severity scoring systems to determine prognosis of pneumonia caused by COVID-19 virus: a multicentric cohort study (Egyptian Journal of Radiology and Nuclear Medicine, 2025)](https://link.springer.com/article/10.1186/s43055-025-01622-x)\n\n---\n*Topic: Encyclopedia › Life and health › Human health and medicine › Clinical assessment and procedures › Medical imaging and radiography › Image analysis and quantitative imaging*\n\n*Initially written Sep 29, 2026 · Edited: Oct 15, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n", "same_as": [], "url": "https://www.edgepedia.org/ct-severity-score", "markdown_url": "https://www.edgepedia.org/ct-severity-score.md", "license": { "name": "Edgepedia Community License 1.0", "url": "https://www.edgepedia.org/edgepedia/license", "summary": "Free with credit, commercial use included. AI training is open to everyone. For other uses, organizations over USD 100M in revenue or 100M monthly users license separately.", "spdx": "LicenseRef-Edgepedia-Community-1.0" }, "credit": "\"CT severity score\", Edgepedia (EdgeChat), https://www.edgepedia.org/ct-severity-score. Edgepedia Community License 1.0.", "credit_md": "\"[CT severity score](https://www.edgepedia.org/ct-severity-score)\", Edgepedia (EdgeChat), [https://www.edgepedia.org/ct-severity-score](https://www.edgepedia.org/ct-severity-score). [Edgepedia Community License 1.0](https://www.edgepedia.org/edgepedia/license).", "credit_html": "\"CT severity score\", Edgepedia (EdgeChat), https://www.edgepedia.org/ct-severity-score. Edgepedia Community License 1.0.", "speakable": "The CT severity score is a semi-quantitative radiological method grading lung involvement on chest CT, widely applied during the COVID-19 pandemic to predict ICU admission and death." }