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Multicentre validation of CT grey-level co-occurrence matrix features for overall survival in primary oesophageal adenocarcinoma.

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Title: Multicentre validation of CT grey-level co-occurrence matrix features for overall survival in primary oesophageal adenocarcinoma.
Authors: O'Shea, Robert1 (AUTHOR), Withey, Samuel J.1,2 (AUTHOR), Owczarczyk, Kasia1,3 (AUTHOR), Rookyard, Christopher1 (AUTHOR), Gossage, James4 (AUTHOR), Godfrey, Edmund5 (AUTHOR), Jobling, Craig6 (AUTHOR), Parsons, Simon L.7 (AUTHOR), Skipworth, Richard J. E.8 (AUTHOR), Goh, Vicky1,9 (AUTHOR) vicky.goh@kcl.ac.uk, Fitzgerald, Rebecca C. (AUTHOR), Edwards, Paul A. W. (AUTHOR), Grehan, Nicola (AUTHOR), Nutzinger, Barbara (AUTHOR), Redmond, Aisling M. (AUTHOR), Abbas, Sujath (AUTHOR), Freeman, Adam (AUTHOR), Smyth, Elizabeth C. (AUTHOR), O'Donovan, Maria (AUTHOR), Miremadi, Ahmad (AUTHOR)
Source: European Radiology. Oct2024, Vol. 34 Issue 10, p6919-6928. 10p.
Subjects: Overall survival, Esophageal tumors, Disease risk factors, Esophageal cancer, Survival rate
Abstract: Background: Personalising management of primary oesophageal adenocarcinoma requires better risk stratification. Lack of independent validation of proposed imaging biomarkers has hampered clinical translation. We aimed to prospectively validate previously identified prognostic grey-level co-occurrence matrix (GLCM) CT features for 3-year overall survival. Methods: Following ethical approval, clinical and contrast-enhanced CT data were acquired from participants from five institutions. Data from three institutions were used for training and two for testing. Survival classifiers were modelled on prespecified variables ('Clinical' model: age, clinical T-stage, clinical N-stage; 'ClinVol' model: clinical features + CT tumour volume; 'ClinRad' model: ClinVol features + GLCM_Correlation and GLCM_Contrast). To reflect current clinical practice, baseline stage was also modelled as a univariate predictor ('Stage'). Discrimination was assessed by area under the receiver operating curve (AUC) analysis; calibration by Brier scores; and clinical relevance by thresholding risk scores to achieve 90% sensitivity for 3-year mortality. Results: A total of 162 participants were included (144 male; median 67 years [IQR 59, 72]; training, 95 participants; testing, 67 participants). Median survival was 998 days [IQR 486, 1594]. The ClinRad model yielded the greatest test discrimination (AUC, 0.68 [95% CI 0.54, 0.81]) that outperformed Stage (ΔAUC, 0.12 [95% CI 0.01, 0.23]; p =.04). The Clinical and ClinVol models yielded comparable test discrimination (AUC, 0.66 [95% CI 0.51, 0.80] vs. 0.65 [95% CI 0.50, 0.79]; p >.05). Test sensitivity of 90% was achieved by ClinRad and Stage models only. Conclusions: Compared to Stage, multivariable models of prespecified clinical and radiomic variables yielded improved prediction of 3-year overall survival. Clinical relevance statement: Previously identified radiomic features are prognostic but may not substantially improve risk stratification on their own. Key Points: • Better risk stratification is needed in primary oesophageal cancer to personalise management. • Previously identified CT features—GLCM_Correlation and GLCM_Contrast—contain incremental prognostic information to age and clinical stage. • Compared to staging, multivariable clinicoradiomic models improve discrimination of 3-year overall survival. [ABSTRACT FROM AUTHOR]
Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Multicentre validation of CT grey-level co-occurrence matrix features for overall survival in primary oesophageal adenocarcinoma.
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  Data: <searchLink fieldCode="AR" term="%22O'Shea%2C+Robert%22">O'Shea, Robert</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Withey%2C+Samuel+J%2E%22">Withey, Samuel J.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Owczarczyk%2C+Kasia%22">Owczarczyk, Kasia</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rookyard%2C+Christopher%22">Rookyard, Christopher</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gossage%2C+James%22">Gossage, James</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Godfrey%2C+Edmund%22">Godfrey, Edmund</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jobling%2C+Craig%22">Jobling, Craig</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Parsons%2C+Simon+L%2E%22">Parsons, Simon L.</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Skipworth%2C+Richard+J%2E+E%2E%22">Skipworth, Richard J. E.</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Goh%2C+Vicky%22">Goh, Vicky</searchLink><relatesTo>1,9</relatesTo> (AUTHOR)<i> vicky.goh@kcl.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Fitzgerald%2C+Rebecca+C%2E%22">Fitzgerald, Rebecca C.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Edwards%2C+Paul+A%2E+W%2E%22">Edwards, Paul A. W.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Grehan%2C+Nicola%22">Grehan, Nicola</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nutzinger%2C+Barbara%22">Nutzinger, Barbara</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Redmond%2C+Aisling+M%2E%22">Redmond, Aisling M.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Abbas%2C+Sujath%22">Abbas, Sujath</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Freeman%2C+Adam%22">Freeman, Adam</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Smyth%2C+Elizabeth+C%2E%22">Smyth, Elizabeth C.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22O'Donovan%2C+Maria%22">O'Donovan, Maria</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Miremadi%2C+Ahmad%22">Miremadi, Ahmad</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. Oct2024, Vol. 34 Issue 10, p6919-6928. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Overall+survival%22">Overall survival</searchLink><br /><searchLink fieldCode="DE" term="%22Esophageal+tumors%22">Esophageal tumors</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+risk+factors%22">Disease risk factors</searchLink><br /><searchLink fieldCode="DE" term="%22Esophageal+cancer%22">Esophageal cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Survival+rate%22">Survival rate</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: Personalising management of primary oesophageal adenocarcinoma requires better risk stratification. Lack of independent validation of proposed imaging biomarkers has hampered clinical translation. We aimed to prospectively validate previously identified prognostic grey-level co-occurrence matrix (GLCM) CT features for 3-year overall survival. Methods: Following ethical approval, clinical and contrast-enhanced CT data were acquired from participants from five institutions. Data from three institutions were used for training and two for testing. Survival classifiers were modelled on prespecified variables ('Clinical' model: age, clinical T-stage, clinical N-stage; 'ClinVol' model: clinical features + CT tumour volume; 'ClinRad' model: ClinVol features + GLCM_Correlation and GLCM_Contrast). To reflect current clinical practice, baseline stage was also modelled as a univariate predictor ('Stage'). Discrimination was assessed by area under the receiver operating curve (AUC) analysis; calibration by Brier scores; and clinical relevance by thresholding risk scores to achieve 90% sensitivity for 3-year mortality. Results: A total of 162 participants were included (144 male; median 67 years [IQR 59, 72]; training, 95 participants; testing, 67 participants). Median survival was 998 days [IQR 486, 1594]. The ClinRad model yielded the greatest test discrimination (AUC, 0.68 [95% CI 0.54, 0.81]) that outperformed Stage (ΔAUC, 0.12 [95% CI 0.01, 0.23]; p =.04). The Clinical and ClinVol models yielded comparable test discrimination (AUC, 0.66 [95% CI 0.51, 0.80] vs. 0.65 [95% CI 0.50, 0.79]; p >.05). Test sensitivity of 90% was achieved by ClinRad and Stage models only. Conclusions: Compared to Stage, multivariable models of prespecified clinical and radiomic variables yielded improved prediction of 3-year overall survival. Clinical relevance statement: Previously identified radiomic features are prognostic but may not substantially improve risk stratification on their own. Key Points: • Better risk stratification is needed in primary oesophageal cancer to personalise management. • Previously identified CT features—GLCM_Correlation and GLCM_Contrast—contain incremental prognostic information to age and clinical stage. • Compared to staging, multivariable clinicoradiomic models improve discrimination of 3-year overall survival. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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