Multicentre validation of CT grey-level co-occurrence matrix features for overall survival in primary oesophageal adenocarcinoma
Authors: O’Shea Robert , Withey Samuel J. , Owczarczyk Kasia , Rookyard Christopher , Gossage James , Godfrey Edmund , Jobling Craig , Parsons Simon L. , Skipworth Richard J. E. , Goh Vicky ,   on behalf of the OCCAMS Consortium, Fitzgerald Rebecca C. , Edwards Paul A. W. , Grehan Nicola , Nutzinger Barbara , Redmond Aisling M. , Abbas Sujath , Freeman Adam , Smyth Elizabeth C. , O’Donovan Maria , Miremadi Ahmad , Malhotra Shalini , Tripathi Monika , Cheah Calvin , Coles Hannah , Eldridge Matthew , Secrier Maria , Devonshire Ginny , Jammula Sriganesh , Davies Jim , Crichton Charles , Carroll Nick , Hardwick Richard H. , Safranek Peter , Hindmarsh Andrew , Sujendran Vijayendran , Hayes Stephen J. , Ang Yeng , Sharrocks Andrew , Preston Shaun R. , Bagwan Izhar , Save Vicki , O’Neill J. Robert , Tucker Olga , Beggs Andrew , Taniere Philippe , Puig Sonia , Contino Gianmarco , Underwood Timothy J. , Grace Ben L. , Lagergren Jesper , Davies Andrew , Chang Fuju , Mahadeva Ula , Ciccarelli Francesca D. , Sanders Grant , Chan David , Cheong Ed , Kumar Bhaskar , Sreedharan Loveena , Soomro Irshad , Kaye Philip , Saunders John , Lovat Laurence , Haidry Rehan , Scott Michael , Sothi Sharmila , Hanna George B. , Peters Christopher J. , Moorthy Krishna , Grabowska Anna , Turkington Richard , McManus Damian , Coleman Helen , Petty Russell D. , Bartlett Freddie , Crosby Tom D. L. 
Published: March 2024

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.